{"as_of":"2026-08-20T05:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2cec858e2258844a27cdabb2958279e5e076644bd75c52d0f49e1ce00c77587f","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:00:43.930036Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.20090/citation-record","integrity":"/paper/2506.20090/integrity","json":"/paper/2506.20090/citation-record.json","paper":"/paper/2506.20090"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.362293Z","title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","venue":null,"work_id":"6aa5efbd-2803-4bcf-830a-ffbe295db0cd","year":2006},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.779738Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:21630a03f08a941b8756bc542917ec4724cf4b04cebff63f9d3d410407a50014","observation_id":"2cd48f94-c824-4704-bc9a-85c36887dfd9","resolution":{"observed_at":"2026-08-06T23:00:44.364978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.355279Z","title":"An introduction to predictive maintenance","venue":null,"work_id":"0de133dc-d480-4f12-99e3-cf4fbb08f917","year":2002},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.782876Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:59e0506eaecbd6e646362fe208c0c0c016912ae08953da965db7ece8d0000d24","observation_id":"80f34854-f0de-4248-8b32-391dd5d151cd","resolution":{"observed_at":"2026-08-06T23:00:44.357894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.348392Z","title":"Selecting an appropriate supervised machine learning algorithm for predictive maintenance","venue":null,"work_id":"90f5ef2d-7f03-4d47-ba82-f9994e6921c3","year":2022},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.785605Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:833db269d08afbaa6895c7054ba0dac143c93d25d78960a784bd3fd9a223ee68","observation_id":"37e4e1db-0613-4e9c-8699-82661f008bc9","resolution":{"observed_at":"2026-08-06T23:00:44.351133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.341299Z","title":"Study on predictive maintenance strategy.International Journal of u-and e-Service, Science and Technology, 9(4):295–300, 2015","venue":null,"work_id":"0ca40d7e-89cd-4dd9-acea-b95a475eac61","year":2015},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.789186Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:b4653b971b6d6f75a3201540b08107fa41cbba64e63d35f22e3a878e2540f93c","observation_id":"0d1fb52f-8b47-491e-a2ff-12e88e12cd68","resolution":{"observed_at":"2026-08-06T23:00:44.344155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.334400Z","title":"Remaining useful life prediction based on deep learning: A survey","venue":null,"work_id":"94d4e1d7-8986-4bee-b5e8-1824451281c3","year":2024},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.791927Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:19846f2d61af5a63f91b77ac12aa07306d0479108d54ab42cb43bfdc665c2705","observation_id":"e1b6e876-5b3f-4452-ae72-b5a42fd70833","resolution":{"observed_at":"2026-08-06T23:00:44.337141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.326260Z","title":"Rul forecasting for wind turbine predictive maintenance based on deep learning","venue":null,"work_id":"23f579a0-4a97-48a4-abbb-6e0b620cadd1","year":2024},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.794724Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:7dc79248d5429c5e673e28ddcbd815c71d8de65b283f3ef2891d6c6d1205b0d4","observation_id":"2e6e2dc2-191e-4c93-8b8c-c4d5a9e57894","resolution":{"observed_at":"2026-08-06T23:00:44.329098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01614","last_updated":"2025-04-14T11:57:40Z","snapshot_observed_at":"2026-08-19T04:36:55.284920Z","submitted_at":"2024-05-02T16:17:29Z","title":"RULSurv: A probabilistic survival-based method for early censoring-aware prediction of remaining useful life in ball bearings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01614","snapshot_observed_at":"2026-08-06T23:00:43.797509Z","title":"A probabilistic estimation of remaining useful life from censored time-to-event data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.797509Z"},"links":{"cited_paper":"/paper/2405.01614","citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:0e1a610474a2dd6568a32c0f180a36428caa56179c332440e516b6eda30375e1","observation_id":"5465d538-d783-4502-ad4c-8956c821ddf7","resolution":{"observed_at":"2026-08-06T23:00:43.797509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.317837Z","title":"Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling","venue":null,"work_id":"413b30b5-af85-4722-b177-72791b785a51","year":2025},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.801014Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:c7d44c4d22095d8107bbdcc33901fd4ad1e847468960d3351c9a1491269e490f","observation_id":"e13a0576-379f-4b60-b78f-8024806552ba","resolution":{"observed_at":"2026-08-06T23:00:44.320990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.310922Z","title":"Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry","venue":null,"work_id":"4df04251-16e4-4ed1-862d-0d021962788b","year":2021},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.804432Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:2920bc626a25b32940591b17c922d4a252ad58f00a0da74c6c848b707fc03ab6","observation_id":"28565fa3-2cfc-4377-8de1-05734e94edee","resolution":{"observed_at":"2026-08-06T23:00:44.313599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.304076Z","title":"Smart predictive maintenance for high-performance computing systems: A literature review","venue":null,"work_id":"69751260-c8fd-435f-b687-f3a1eead507c","year":2021},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.806935Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:97f799958363c448c0a673ad569f0764bb2c1bc0951665201967d0cc825993b8","observation_id":"f5d8f9bc-f837-4f05-8dc5-bf8e1299d816","resolution":{"observed_at":"2026-08-06T23:00:44.306726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.296213Z","title":"Predictive maintenance in the industry: A comparative study on deep learning-based remaining useful life estimation","venue":null,"work_id":"d2feb375-282d-4cc9-95f3-142c6a5ef0ad","year":2023},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.810346Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:e8075ff1f02dccbca68e46b9b36d3bd7e47af8ec0a8c499d02629791a5562a47","observation_id":"124cd03c-ae1e-4494-a160-b2d783017158","resolution":{"observed_at":"2026-08-06T23:00:44.298921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.289126Z","title":"A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems.IEEE Transactions on Industrial Informatics, 2023","venue":null,"work_id":"e28df47f-ccd9-4beb-9ddb-9bf786f4641b","year":2023},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.812770Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:e513c8aaf5da0dc9a57d3f858793f3b9e6042c2318d59b68f2decb9b7c5c010a","observation_id":"3ee5e8aa-b122-409d-80ed-207eb0c4346a","resolution":{"observed_at":"2026-08-06T23:00:44.291916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.281182Z","title":"Improve predictive maintenance through the application of artificial intelligence: A systematic review","venue":null,"work_id":"dd957fcc-d430-4687-b408-87cf57ff28db","year":2024},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.815320Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:fc58e3ca7aa95cfe57426ccf98ec565b84c2539e425e73026c27ce1181364c6c","observation_id":"57de2cdf-1633-4f1e-84a9-3fc25333c268","resolution":{"observed_at":"2026-08-06T23:00:44.284206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.05239","last_updated":"2020-05-11T16:30:54Z","snapshot_observed_at":"2026-08-19T04:38:23.088289Z","submitted_at":"2020-05-11T16:30:54Z","title":"System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis","version":1},"cited_work":{"arxiv_id":"2005.05239","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.05239","snapshot_observed_at":"2026-08-06T23:00:43.984259Z","title":"System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis","venue":"cs.AI","work_id":"f2e86f1d-6e29-4db5-ad22-8706bc5f2c79","year":2020},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.817712Z"},"links":{"cited_paper":"/paper/2005.05239","citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:088290f84571d35ff84fc1010fed9bb2f5c63c056e4f98878c3aea4192252f68","observation_id":"aaa1bb09-cc8d-4b1f-92fc-3ef8388af50d","resolution":{"observed_at":"2026-08-06T23:00:43.988407Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.273255Z","title":"Predictive maintenance in the automotive sector: A literature review","venue":null,"work_id":"af2f0a66-37a9-4aaa-8b2b-ffa56e782936","year":2021},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.821425Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:5afdf1ff5d71cd32b0d7c8b35afaf962ae3026707b809868eb6d79d692eaa90b","observation_id":"a87e446d-f7c7-4e47-860a-0983eedd7f6b","resolution":{"observed_at":"2026-08-06T23:00:44.275943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.266593Z","title":"Deep learning models for predictive maintenance: A survey, comparison, challenges, and prospect","venue":null,"work_id":"ad7ccc0b-e1d8-4d9b-af8e-5a4ca15523ae","year":2020},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.823853Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:feca450246ce851b995a4b6684a8fc54c48c41cb8cfe9bdd95a9373c94119db2","observation_id":"de371829-87c7-4345-9994-18d30d4565aa","resolution":{"observed_at":"2026-08-06T23:00:44.269175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.259665Z","title":"Revolutionizing system reliability: The role of ai in predictive maintenance strategies","venue":null,"work_id":"4b91fd1c-38eb-4582-b1a1-728c23cf9168","year":2024},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.826436Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:c948977e1e1ca5a41f2c7fc99981546d3ae76251c483e34643468f5776ff0983","observation_id":"30fd62f9-3b6f-4b7f-9aab-29049cc6198d","resolution":{"observed_at":"2026-08-06T23:00:44.262341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.252789Z","title":"Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics","venue":null,"work_id":"bee409cc-85b5-4b92-a561-84ec9e895dc4","year":2021},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.829842Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:d00534a5a3a01ab3398b6cef35ce95a4ed7f508164f32907d303ba39d34c6bef","observation_id":"d02dade4-86f3-4225-8f39-8cd818ac584c","resolution":{"observed_at":"2026-08-06T23:00:44.255523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.245021Z","title":"Predictive maintenance modelling for through-life engineering services","venue":null,"work_id":"08309a2d-d9d2-4415-88bb-b94c6039956c","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.832491Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:e08f22befdb7ef158c1b6d5a747e5b897e82f7baef2d4aa9e3dadc604265108b","observation_id":"30a88f7f-0e31-45dd-aef9-0f981bf4823b","resolution":{"observed_at":"2026-08-06T23:00:44.248584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.238186Z","title":"Maintenance today and future trends","venue":null,"work_id":"b19fdb3e-2c16-4daf-a308-7a3a84b05945","year":2010},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.835020Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:811d6286bdf0353010b08d62e008b2ae2247df6a841da0a7913277b147eee34d","observation_id":"13393e45-7c5f-43d7-86ba-ef79a6e092d8","resolution":{"observed_at":"2026-08-06T23:00:44.240758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.231279Z","title":"Predictive maintenance, its implementation and latest trends","venue":null,"work_id":"5388f284-6669-4d01-8233-b05fc98d4947","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.837527Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:5b6f6eea1a67b0c1f178c76abb551fa4a53ba1bdd3f366860079819a8064e0f2","observation_id":"fb9d8f4c-f9f5-4d4f-8cc1-3cdf4864716d","resolution":{"observed_at":"2026-08-06T23:00:44.234038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.224193Z","title":"Smart predictive maintenance for high-performance computing systems: a literature review","venue":null,"work_id":"404a2345-acbe-4894-aa1f-f7cb3792b2b1","year":2021},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.840103Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:6f97f81a18cd58eb545c8964b4deceadfd3619a1dfc8422aa92d0522e032ab9d","observation_id":"d31fe7d9-9aa1-45dd-bee7-0d5793a7adda","resolution":{"observed_at":"2026-08-06T23:00:44.227034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.217467Z","title":"A prognostic algorithm for machine performance assessment and its application","venue":null,"work_id":"1eb3f13a-8736-4fd5-a415-4ba285f1bedf","year":2004},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.842849Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:17ae07e0d36092afef954406e468a81591ff3e77774211e1d77eefb9ec82d35d","observation_id":"04cee006-b40f-4b87-af9b-06b830be05a4","resolution":{"observed_at":"2026-08-06T23:00:44.220089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.210566Z","title":"Remaining useful life estimation of critical components with application to bearings","venue":null,"work_id":"9882d74c-4c70-4610-a188-130a134e03d5","year":2012},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.846642Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:f406e9567b61932c8c34ed8840c188f1832144d5497ffca9d490696a317825c2","observation_id":"d844ee93-79e7-4de7-9225-a06e045fb65d","resolution":{"observed_at":"2026-08-06T23:00:44.213167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.203690Z","title":"Current status of machine prognostics in condition-based maintenance: a review","venue":null,"work_id":"2e1fcc50-3072-469c-9bf5-338df4608380","year":2010},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.849102Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:37e061f929e0be4e9394c1e2d222bcacce943bee894c0a590ecbd05da7c45f02","observation_id":"3af61203-3265-4c6d-b37d-06b49796ee09","resolution":{"observed_at":"2026-08-06T23:00:44.206337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.195851Z","title":"A survey of data-driven prognostics","venue":null,"work_id":"5568ce26-be27-4867-a4d1-ae2f29d6412b","year":2005},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.851575Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:d5ec2915502f6cbb0b1314e6d015e58baaf17c3813345e5f28700dd2fa78e7f0","observation_id":"39d9f7be-d7ef-4672-90a8-03eae51ea40c","resolution":{"observed_at":"2026-08-06T23:00:44.198365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.188986Z","title":"A model to predict the residual life of rolling element bearings given monitored condition information to date","venue":null,"work_id":"2da55fbb-d14b-4c06-a728-7881a53d256e","year":2002},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.853824Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:d70f0259c30b6de2fd01c6aae92f1de54babd9e7b347ead1263742d955205804","observation_id":"15d9e30b-784e-45ae-9bd0-ec7cd8ca5e0e","resolution":{"observed_at":"2026-08-06T23:00:44.191751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.181897Z","title":"Time series prediction techniques for estimating remaining useful lifetime of cutting tool failure","venue":null,"work_id":"b1678439-cd07-4860-994f-9e23285201dd","year":2014},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.857232Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:053236c87cfb3a97980d1f15007483b015a6d73823e385645a122d3b7fa94f01","observation_id":"add3298f-68b0-4f06-9236-54c0e2f40268","resolution":{"observed_at":"2026-08-06T23:00:44.184629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.174844Z","title":"Remaining useful life prediction of grinding mill liners using an artificial neural network","venue":null,"work_id":"7df53c42-7172-46ac-9db5-4dd3252441d8","year":2013},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.859720Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:79fb137f478bea4656405f35b47fa6019d3f25c0b23837d0c998d9679ddc0a4f","observation_id":"300327d9-049d-44d3-97c6-a3b9de57a42e","resolution":{"observed_at":"2026-08-06T23:00:44.177485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.167698Z","title":"A practical approach to combine data mining and prognostics for improved predictive maintenance","venue":null,"work_id":"ba25f650-7807-4604-974c-c68e402c341d","year":2009},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.862163Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:c0f1a0357c3a9eb1d28f4eeacddf7cc334a7d5d543327d132d7760e8637ac5c1","observation_id":"ffba4b3e-d299-4b49-999c-1ad72433b88d","resolution":{"observed_at":"2026-08-06T23:00:44.170547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.160543Z","title":"Time series data prediction using sliding window based rbf neural network","venue":null,"work_id":"05ac72df-a815-448a-ab58-8925c9789ffb","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.864574Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:416edb81bfb9938b186ee110e2a7547d2ea901d61717e3c669411afa1bc890dc","observation_id":"6c6e7a75-308f-422f-adb4-eca575d0ab31","resolution":{"observed_at":"2026-08-06T23:00:44.163368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.153646Z","title":"A comparison of three data-driven techniques for prognostics","venue":null,"work_id":"9c75ab38-c59e-4aae-8d99-4ab150846d33","year":2008},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.866929Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:97dc8e9f4b918243f7776453e28374e21ed2ced59b6a8f6bc598e3cdc74c940e","observation_id":"fe51602a-ba0d-47a6-959e-10571d4a1f7f","resolution":{"observed_at":"2026-08-06T23:00:44.156183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.146528Z","title":"A data-driven failure prognostics method based on mixture of gaussians hidden markov models","venue":null,"work_id":"12788e44-8819-4f2d-bb1d-bfd3425db82d","year":2012},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.869419Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:c6b45708bec2641d2c3a41af7c268d62d8bb41bde32b3373bfbf984bf77429f8","observation_id":"03ee6e9b-fd1d-4623-9cdd-5fb19d402971","resolution":{"observed_at":"2026-08-06T23:00:44.149269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.139470Z","title":"A new framework for remaining useful life estimation using support vector machine classifierf","venue":null,"work_id":"806517db-e912-4912-a5c2-0eefa17b1a95","year":2013},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.872011Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:899af0798284881ebfd9771433bac7e04ce53f7e39e4a084bdb363dd7a8584ff","observation_id":"7905b20d-a551-461b-a788-bf93af77fbb9","resolution":{"observed_at":"2026-08-06T23:00:44.142214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.132579Z","title":"Predictive maintenance decision using statistical linear regression and kernel methods","venue":null,"work_id":"dc104cbf-be99-4d56-9005-c99f83ed6ced","year":2014},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.874659Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:7c2543a27bf0c414d1500740d3a39bf28c0006a115f2f952fa00c19315e01431","observation_id":"14ed2d02-d5b1-41e6-b6fa-86a0c850b8e3","resolution":{"observed_at":"2026-08-06T23:00:44.135263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.125085Z","title":"A predictive maintenance system for epitaxy processes based on filtering and prediction techniques","venue":null,"work_id":"805b7c0b-228a-4017-be43-a257bb9ccc07","year":2012},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.877000Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:716fae50c2c95dbf0aa2bb3d5a322fc4ef8d3dc5d8e4ac78bb86b542704adb05","observation_id":"41499693-4040-4fa2-823b-bc0cff6adec9","resolution":{"observed_at":"2026-08-06T23:00:44.127940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.118045Z","title":"Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine","venue":null,"work_id":"cb93e98d-e051-4738-b1b1-7f505332180f","year":2012},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.879576Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:53ddca871d333427d8cabaf1f0f3b7f8774d1b6726f3bcd0d458fc860ef1ac17","observation_id":"a91bfb57-dc21-42be-a246-29b1fe1b2f40","resolution":{"observed_at":"2026-08-06T23:00:44.120870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.110658Z","title":"Feature signature prediction of a boring process using neural network modeling with confidence bounds","venue":null,"work_id":"a99c0280-ad5b-4e8c-9ead-36ab807bd529","year":2006},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.882071Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:0066ca6ed1ae94e68255155c37fb775a0f5f33b00cd9c8d4cafb8cf1776c7906","observation_id":"52ded788-e6a2-4066-811b-7cdd138eff09","resolution":{"observed_at":"2026-08-06T23:00:44.113487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.102905Z","title":"A predictive tool for remaining useful life estimation of rotating machinery components","venue":null,"work_id":"b42462fd-5728-42db-8dd1-47dd5542e38c","year":2005},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.884517Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:38ab17df5e7d26b3a0040d68e617cb6916c3d444cfb7b95b157e5cf7ebfcc227","observation_id":"92448685-7b19-4eec-abcc-8c641336c391","resolution":{"observed_at":"2026-08-06T23:00:44.105645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.095889Z","title":"Dealing with time-series data in predictive maintenance problems","venue":null,"work_id":"1b51d809-cad0-4761-830c-13d77526b978","year":2016},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.887122Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:e61ed24fc3dc3b31db6ffddea34b5abcea7edc6f7d21ddccffb367c272d7e156","observation_id":"085ea147-2e24-41c5-aca8-924ad3e3a4d3","resolution":{"observed_at":"2026-08-06T23:00:44.098671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.088775Z","title":"Remaining useful life estimation in prognostics using deep convolution neural networks","venue":null,"work_id":"18abed95-cd05-4021-8a0a-1fe0361e47e3","year":2018},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.890190Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:20c3a2d156e26cad7482813c3a1b2f63aaf19d28e933df958904bcc1139b2248","observation_id":"3fe48407-fffc-49ba-8568-045d0d6e2e19","resolution":{"observed_at":"2026-08-06T23:00:44.091475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.081432Z","title":"Multi-bearing remaining useful life collaborative prediction: A deep learning approach","venue":null,"work_id":"5e77d4c2-7a91-4546-adbb-8256d70599ac","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.892684Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:31c3bb07e50c661e3c5134c43fbf4d3aef36694ad45a76dcc82649ebc0dc036a","observation_id":"50bc1c81-e29b-4241-b7c3-4310f94d6cb9","resolution":{"observed_at":"2026-08-06T23:00:44.084265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.074442Z","title":"Deep convolutional neural network based regression approach for estimation of remaining useful life","venue":null,"work_id":"71fda342-7ae0-48f5-95bb-10f6136a1c8a","year":2016},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.895267Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:d50b63afe6f2906f493e1873f6c2bfffd022ff109c5f87fcd31f0eeacc694078","observation_id":"116209c4-75da-4b80-bbc0-7c5dd2df8753","resolution":{"observed_at":"2026-08-06T23:00:44.077252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.066934Z","title":"Long short-term memory network for remaining useful life estimation","venue":null,"work_id":"c0defd94-db17-43a0-8fc8-8d7f851d4045","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.897737Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:7640b80ca9a4827df4ea4dc127db48000cd687c5e37c3a3dca447273d0e188f5","observation_id":"1995d643-41f7-431c-b82b-67507e759418","resolution":{"observed_at":"2026-08-06T23:00:44.069846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.059516Z","title":"Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network","venue":null,"work_id":"d4ddeeb8-c8dd-452a-9bb3-88a2b950efb3","year":2016},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.900384Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:a1bc40f43950cb37e664ead22267b50c8827bcd5802b321970d0a808d222957a","observation_id":"8bfcf223-ebab-44fb-a2bb-ed2e5a7cec16","resolution":{"observed_at":"2026-08-06T23:00:44.062343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.052347Z","title":"Predicting computer system failures using support vector machines","venue":null,"work_id":"760081e8-0207-40f5-bfe2-4eff87df52be","year":2008},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.902818Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:3f40500e6c1ad7835c9edbc27814551883707749646dc3a5fb93f0801b6771ad","observation_id":"79b0a934-374d-4a89-a610-a5a3d62265d5","resolution":{"observed_at":"2026-08-06T23:00:44.055056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.045065Z","title":"Sequence based classification for predictive maintenance","venue":null,"work_id":"06cab9e0-6862-433d-929d-6aaddb0d66c4","year":null},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.905133Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:1e1c9dba47b9d1b7ee7cc2cc587ecaf8ac9f77fe225b230b3d95349fe95ba8c3","observation_id":"0a3eafdc-b1ee-4b6f-93b8-e587fe80cc90","resolution":{"observed_at":"2026-08-06T23:00:44.047992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.037622Z","title":"Analysis of truck compressor failures based on logged vehicle data","venue":null,"work_id":"2619a260-cb2b-4fe3-88da-073b5313a52e","year":2013},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.907824Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:f88a5e9b9c3510a599f972f830e94c2194b3325f09805fe224aac3ea81c64cfd","observation_id":"12195850-8d9d-494a-951f-21dd24e131a3","resolution":{"observed_at":"2026-08-06T23:00:44.040557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.03633","last_updated":"2017-01-13T11:31:35Z","snapshot_observed_at":"2026-08-19T04:38:22.581000Z","submitted_at":"2017-01-13T11:31:35Z","title":"A dissimilarity-based approach to predictive maintenance with application to HVAC systems","version":1},"cited_work":{"arxiv_id":"1701.03633","doi":null,"metadata_source":"pith","pith_arxiv_id":"1701.03633","snapshot_observed_at":"2026-08-06T23:00:43.974232Z","title":"A dissimilarity-based approach to predictive maintenance with application to HVAC systems","venue":"cs.LG","work_id":"0e57742c-b3b8-46ce-b4c1-67ee87cd855d","year":2017},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.910317Z"},"links":{"cited_paper":"/paper/1701.03633","citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:2f8078e71c4e934ce07587181fd5aeec3a48a205a536dd047e2d21a9572333ad","observation_id":"58cc106c-9be4-4a2a-9e67-849ae8d8f038","resolution":{"observed_at":"2026-08-06T23:00:43.977340Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.030094Z","title":"Machine learning for predictive maintenance: A multiple classifier approach","venue":null,"work_id":"aa441add-4cb0-4380-b97e-c677e71e9ea3","year":2015},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.913032Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:25649e29bdcc45e39a34d1ab9ea158da30952e5a5a791e9fe762dc82e5b415ba","observation_id":"5b65c0e3-76b6-4007-acc1-ba56994dcecf","resolution":{"observed_at":"2026-08-06T23:00:44.032833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06022","last_updated":"2018-04-17T03:03:06Z","snapshot_observed_at":"2026-08-19T04:38:22.036463Z","submitted_at":"2018-04-17T03:03:06Z","title":"Predicting Future Machine Failure from Machine State Using Logistic Regression","version":1},"cited_work":{"arxiv_id":"1804.06022","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.06022","snapshot_observed_at":"2026-08-06T23:00:43.964060Z","title":"Predicting Future Machine Failure from Machine State Using Logistic Regression","venue":"stat.AP","work_id":"4ba8388c-3ef6-4988-b64c-db058ee328b1","year":2018},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.915476Z"},"links":{"cited_paper":"/paper/1804.06022","citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:05f09b3a9ef3de5cfd4c33ddc917f0d88096cdfe294fa53dfe62288ba200ea92","observation_id":"97fa9f87-69ed-42fa-8cc0-25bacab5e18d","resolution":{"observed_at":"2026-08-06T23:00:43.967152Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.13574","last_updated":"2022-04-30T08:58:04Z","snapshot_observed_at":"2026-08-20T02:25:41.579673Z","submitted_at":"2022-04-28T15:44:12Z","title":"An Explainable Regression Framework for Predicting Remaining Useful Life of Machines","version":2},"cited_work":{"arxiv_id":"2204.13574","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.13574","snapshot_observed_at":"2026-08-06T23:00:43.950756Z","title":"An Explainable Regression Framework for Predicting Remaining Useful Life of Machines","venue":"cs.LG","work_id":"7ee7e457-e8b0-4c72-95f1-550196d0c18b","year":2022},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.918220Z"},"links":{"cited_paper":"/paper/2204.13574","citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:81b325a7b8ec9253d1cf9b5b95d0d0d053a1349c6ec8fb3e9153483c04ac11a8","observation_id":"5d4efd4e-ef19-48c9-bcd3-6e1688b45bff","resolution":{"observed_at":"2026-08-06T23:00:43.956701Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.022776Z","title":"Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics","venue":null,"work_id":"ed43ea30-ac02-4eff-919c-5b2f1dec6a5f","year":2016},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.921690Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:9170da4c509c149fdafe794d8db823923c405b67416d52777c2659f7bc036956","observation_id":"3a13fcb3-b835-4e74-a2af-5e9cc8fa1975","resolution":{"observed_at":"2026-08-06T23:00:44.025556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.015403Z","title":"Predictive maintenance for aircraft engine using machine learning: Trends and challenges","venue":null,"work_id":"33224551-1266-495f-889c-f029a09d9a6c","year":2025},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.924195Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:9140dc30dd2162b1758c03ac9a064299cb17390717a219875c4b429ddf7518a7","observation_id":"4ab5c3a7-7528-4d83-9ff4-955976f09ae7","resolution":{"observed_at":"2026-08-06T23:00:44.018302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:44.007256Z","title":"Addressing the curse of imbalanced training sets: one-sided selection","venue":null,"work_id":"03d4bc83-fc6e-4991-a983-62b46ae1f762","year":1997},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.926710Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:59e7549a73cede01adfd8b998468961f8dd907b1e747f022d5f0010046d224e6","observation_id":"ff439ab8-ba6b-41dc-880f-b6d439de66a1","resolution":{"observed_at":"2026-08-06T23:00:44.010228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:00:43.999576Z","title":"Remaining useful life estimation","venue":null,"work_id":"2f317e58-401d-4b55-b61d-e9ee8d763aac","year":2014},"citing_paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T23:00:43.930036Z"},"links":{"citing_paper":"/paper/2506.20090"},"observation_digest":"sha256:fac978952d76f6f19a9cd598035c4c5d4a7bc72239519456dd66c010aa6354e2","observation_id":"362cf5b7-96f9-4add-b988-fdef2df595e0","resolution":{"observed_at":"2026-08-06T23:00:44.002514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.20090","last_updated":"2025-06-25T02:22:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T04:37:14.241707Z","submitted_at":"2025-06-25T02:22:23Z","title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":4,"verified_fuzzy":51},"total_outbound_references":56},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2506.20090."}