{"as_of":"2026-08-16T10:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9d40d650df599e1938c9c1c9c7f9b18f403da75214cd8ddf818e926a2d210f21","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:57:21.416884Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T00:35:35.146580Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T08:19:44.620254Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"cited_work":{"arxiv_id":"2412.15998","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.15998","snapshot_observed_at":"2026-07-04T08:19:44.620254Z","title":"Cnn-lstm hybrid deep learning model for remaining useful life estimation.arXiv preprint arXiv:2412.15998, 2024","venue":null,"work_id":"531182ed-fc88-432c-9efd-07d5032b9e5b","year":2024},"citing_paper":{"arxiv_id":"2606.22258","last_updated":"2026-06-20T23:10:04Z","snapshot_observed_at":"2026-08-15T00:10:16.401526Z","submitted_at":"2026-06-20T23:10:04Z","title":"From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-26T11:48:11.845671Z"},"links":{"cited_paper":"/paper/2412.15998","citing_paper":"/paper/2606.22258"},"observation_digest":"sha256:392970ebaa883f6c0d7f5f0d2fc69edeecf8cbca2cdd730b470970d05a052830","observation_id":"3ccaf0eb-b0c7-45e5-84e3-7fd4c47deda8","resolution":{"observed_at":"2026-07-04T08:19:44.621721Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15998","snapshot_observed_at":"2026-08-11T00:35:35.146580Z","title":"CNN-LSTM hybrid deep learning model for remaining useful life estimation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07582","last_updated":"2026-08-05T04:32:30Z","snapshot_observed_at":"2026-08-16T09:13:31.734271Z","submitted_at":"2026-08-05T04:32:30Z","title":"Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T00:35:35.146580Z"},"links":{"cited_paper":"/paper/2412.15998","citing_paper":"/paper/2608.07582"},"observation_digest":"sha256:a8ca465bc90c4947a529821f156b3f096b9550293447e16a149aea85cb7bb434","observation_id":"0977f2c4-a195-4b17-96de-13773a1f2ea4","resolution":{"observed_at":"2026-08-11T00:35:35.146580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.15998/citation-record","integrity":"/paper/2412.15998/integrity","json":"/paper/2412.15998/citation-record.json","paper":"/paper/2412.15998"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:57:22.907444Z","title":"Accurate RUL estimation plays a crucial role in Predictive Maintenance applications","venue":null,"work_id":"8feccd28-b568-4169-b72b-d4683d2e64b3","year":2024},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.018793Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:651bf2de70b8e1585a57a54e8a7e2d9b73b93bcf9a5e724ffd728975b38e85ad","observation_id":"63949598-c255-407a-bea8-3e0c8b911df1","resolution":{"observed_at":"2026-08-11T10:57:22.916969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.886522Z","title":null,"venue":null,"work_id":"a7e335c5-f195-441e-a17d-70251ed2e247","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.026672Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:fc02edf46ad19fd08c9128961278172a2a60b2a677658866d41e2766b21cb45a","observation_id":"2da5b642-cfbe-4d18-a5f3-a40bf94a070e","resolution":{"observed_at":"2026-08-11T10:57:22.895267Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.835671Z","title":"CNN have great potential to identify the various salient patterns of sensor signals","venue":null,"work_id":"d18c3051-0681-4e4a-9cfd-7f94f08d6d47","year":2024},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.054003Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:ecfa9a12ee261a2c3e9b1d2fa08b92e3b40e0017b150bec6b1764645b385fa1d","observation_id":"f1e5528d-297b-4d0c-a78e-7391844add2a","resolution":{"observed_at":"2026-08-11T10:57:22.841045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.801853Z","title":"Remaining Cycles,","venue":null,"work_id":"871329a9-bfbe-4655-a155-637ebd0ed6ea","year":2024},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.065186Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:0b545a7407037072d9a87ba9044187c6c77356b319edb8da91f72b214dd82ac4","observation_id":"e0dea3c2-80aa-4c3a-9914-986563c9bfd0","resolution":{"observed_at":"2026-08-11T10:57:22.810420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.767283Z","title":null,"venue":null,"work_id":"2f6f5554-0432-4c42-968b-9924a6641f95","year":2024},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.079385Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:4a31a7143855d39a1eae02e3d372c9a4d0ede06b5c262e0add92207157bd4524","observation_id":"e91690f3-54ba-45e3-a095-cc8642a0e53c","resolution":{"observed_at":"2026-08-11T10:57:22.782526Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.742562Z","title":"Our experiments on C-MAPSS dataset showed that our proposed model outperforms other approaches and gives the best performance in RUL estimation","venue":null,"work_id":"e89fe741-e35b-4253-8dca-2a1c3361a6b7","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.086905Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:0083299271a517975e1e850f30277f7e9f4beb55e6966fd8cb781271852ed5c4","observation_id":"a8d32c65-0cb1-4a53-9c08-d95460d3358e","resolution":{"observed_at":"2026-08-11T10:57:22.749853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.712980Z","title":"REFERENCES :","venue":null,"work_id":"118a23dd-2f17-441f-821d-f09be2b9631e","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.095313Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:2e1503a983d55dac0a587238eb3ab625e5f7b76f6d58482e656108c1fa6fe0bb","observation_id":"cc2b2d4d-efd5-4aa4-83f9-7eb8f90b899f","resolution":{"observed_at":"2026-08-11T10:57:22.719693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.443105Z","title":"XGBoost Documentation","venue":null,"work_id":"a9767a6b-94bf-46aa-8f35-13303346060c","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.170258Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:9fc60427a071ef14f3aaaf732c4cc8bae71f949dc0a88c17be63b06695d987f4","observation_id":"877e3795-46bd-4ea4-9d4e-5836e6bb16c8","resolution":{"observed_at":"2026-08-11T10:57:22.449025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.675151Z","title":"A generic conceptual simulation model for maintenance systems,","venue":null,"work_id":"30104ff4-16c2-4f65-bcd3-b182f870b227","year":2001},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.101960Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:b2fa6eb9e048bf5887302dd1dae0eba472b27d9d4222a207ebadca3d62733481","observation_id":"ad1846f9-bf81-45a3-9f28-6bf94e2ac973","resolution":{"observed_at":"2026-08-11T10:57:22.689083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.632289Z","title":"Remaining useful life estimation–a review on the statistical data driven approaches,","venue":null,"work_id":"a3a154d4-a4bb-48a1-80e5-60031b869202","year":2011},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.110120Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:0303dd55c9b2b5ab5e4e26cec9106a99d82355f953821088f822fd69ae022a0c","observation_id":"75c0c312-fa9f-4252-95d5-4ef9443c9dcd","resolution":{"observed_at":"2026-08-11T10:57:22.642080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.612724Z","title":"Recurrent neural networks for remaining useful life estimation","venue":null,"work_id":"5c469914-93bd-4030-a6e0-06b42f5b9f8d","year":2008},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.117989Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:304dec368bef5eff8f276fed66005d958c3905fd78aa0282b91dbde9280ea893","observation_id":"50f69fba-2384-4882-98f5-ef1a007b735e","resolution":{"observed_at":"2026-08-11T10:57:22.618264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.573018Z","title":"Deep convolutional neural network based regression approach for estimation of remaining useful life,","venue":null,"work_id":"15f9afe1-f2bd-43c3-b7f7-57888197cdb4","year":2016},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.131257Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:02592a44c9646799ee112a6f2c90c43015d8dcdf1610df21aa9fa27348354c1d","observation_id":"92cd1f17-da70-4cde-ac6f-3c04b6c1c91d","resolution":{"observed_at":"2026-08-11T10:57:22.586079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.523006Z","title":"Long Short -Term Memory Network for Remaining Useful Life Estimation,","venue":null,"work_id":"9760d674-a228-4c8c-a431-0ca64b6cafae","year":2017},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.139504Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:45935e2783d65484805e23dadb3040f6c7df6bbd911f0fc8ea79e9823342474d","observation_id":"3db90f60-edbe-41e9-aa35-0e3ef6988491","resolution":{"observed_at":"2026-08-11T10:57:22.535109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.489958Z","title":"Random Forests","venue":null,"work_id":"95f694fe-e2e2-415e-aa7c-746cd901ba1f","year":2001},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.148403Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:6e57b9cc204c6f365a3abd7f1f09e47dc372152a67afcc01f9363137dc1605a0","observation_id":"8ba6c275-226d-40b3-9fc4-66234f977de1","resolution":{"observed_at":"2026-08-11T10:57:22.501150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.464435Z","title":"XGBoost: A Scalable Tree Boosting System","venue":null,"work_id":"79e3c6a4-2357-4f17-a6bf-70432a2515f3","year":2016},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.160625Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:f6f725541bd82be7670abcf842579439d2838bf283f7b69b5977f579e26d84f1","observation_id":"f36a8525-6db2-48ee-82be-691bd1b3c327","resolution":{"observed_at":"2026-08-11T10:57:22.473222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.160064Z","title":"Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations","venue":null,"work_id":"beeebe10-4e27-4951-b087-d05da6e28820","year":2022},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.240788Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:3909adb6ec782fc6836dda0a9647f17b91ee31cd43d2d221355f31abd9b83387","observation_id":"9b0f54e5-017e-4c48-974c-a6253842db79","resolution":{"observed_at":"2026-08-11T10:57:22.172546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.416765Z","title":"Scikit-learn Documentation: MLPRegressor","venue":null,"work_id":"4c664822-7de5-466c-94ec-9fd808de7291","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.178614Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:d91eaf8dd5f6092ba7fafed01a87f453f11fa77e6ae7085f44c3b1890faefd52","observation_id":"38f4dc9a-66c1-4944-9994-1d6506873be5","resolution":{"observed_at":"2026-08-11T10:57:22.429580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.379847Z","title":"The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases,","venue":null,"work_id":"733244d6-da6a-4801-ac8f-dc508440fef2","year":2020},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.187107Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:89280426d0929c1636494851c2f839bd7af3851e98bae40eb4d95ff72fb419f2","observation_id":"f3032f45-c5a1-4c6e-9987-fc9a4d115141","resolution":{"observed_at":"2026-08-11T10:57:22.390461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.322822Z","title":"Hyperdimensional Data Analysis Using Parallel Coordinates,","venue":null,"work_id":"0f62508b-049e-4395-b272-f1297ef325ce","year":1990},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.193599Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:8a7e66d6363b6152cea248a61d88f9873e51d41b4929b9592938f15936461a2a","observation_id":"34c7edc3-7b54-4614-b12f-8f6493afcdda","resolution":{"observed_at":"2026-08-11T10:57:22.351532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.287972Z","title":"Log based predictive maintenance","venue":null,"work_id":"cd74c8e2-a749-4fdc-a5e5-95499bb770fd","year":null},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.199700Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:a701f35d46a2e12006293bec1f0308914b716ebe5637724ef37ec21ef57e6346","observation_id":"86de695b-89d1-44fb-bd4f-c83ccdd35ed9","resolution":{"observed_at":"2026-08-11T10:57:22.297174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.10996","last_updated":"2021-11-22T07:56:06Z","snapshot_observed_at":"2026-08-16T06:36:37.566341Z","submitted_at":"2020-11-22T12:12:14Z","title":"Predictive maintenance on event logs: Application on an ATM fleet","version":4},"cited_work":{"arxiv_id":"2011.10996","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.10996","snapshot_observed_at":"2026-08-11T10:57:21.475138Z","title":"Predictive maintenance on event logs: Application on an ATM fleet","venue":"cs.LG","work_id":"42ed0108-e588-470e-8500-4af2ffa94885","year":2020},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.211943Z"},"links":{"cited_paper":"/paper/2011.10996","citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:bc1c5f4004c51b906bcdfadb3231422b80ae225c261c54fdb8050939f6795634","observation_id":"24f802dd-953f-48a4-821c-9965090cbbae","resolution":{"observed_at":"2026-08-11T10:57:21.494360Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.244771Z","title":"Vibration analysis for IOT enabled predictive maintenance","venue":null,"work_id":"d17f8b63-e4e3-4b06-9d04-98ac2d324c70","year":2017},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.223000Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:ab06e85c9a5d53ec5e46e523808f39a02630e7ab26a0f52268539b67868b3f88","observation_id":"e50c350b-7501-4bad-b989-15c14c05d650","resolution":{"observed_at":"2026-08-11T10:57:22.269346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.204033Z","title":"Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry","venue":null,"work_id":"1195291a-3262-4153-8842-22b900aa0bae","year":2022},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.230530Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:34b8cdc50eaec795f3ca0a02e943e6b4e9a3403f1ee6e319bf2d0fc2dcf94545","observation_id":"f95e7192-632c-469c-a92e-f3a8d9fc97b9","resolution":{"observed_at":"2026-08-11T10:57:22.212204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.875848Z","title":"Performance benchmarking and analysis of prognostic methods for cmapss datasets","venue":null,"work_id":"6e304436-73cd-4882-9296-98291a2ec5e0","year":2014},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.328398Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:6ba201daf5b61c9b6ab10d628d2d93698957a8bc9938ed5f50b87a9a189443e5","observation_id":"6bf95a00-4325-4593-852c-f013f7d05bf6","resolution":{"observed_at":"2026-08-11T10:57:21.897999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.121936Z","title":"Support-vector networks","venue":null,"work_id":"752b65ef-2c45-4984-89aa-77c71ae128bb","year":1995},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.251896Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:a8d356a247c7f2ce5629fafc0ccaefebfde033f0af897cfc68d76b213eda8952","observation_id":"7f2c1cf6-a0b7-4862-9723-290f8e46ac49","resolution":{"observed_at":"2026-08-11T10:57:22.133683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.090663Z","title":"A similarity-based prognostics approach for remaining useful life estimation of engineered systems","venue":null,"work_id":"385d3214-0533-4160-bb2c-89a2383f5bba","year":2008},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.265125Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:000fbafc3d83453271ad0ccd57e08b3ac5ed1d16cccce7809e68b6f694520da6","observation_id":"c43a2d0d-3d1e-47ec-a711-06d772d007e3","resolution":{"observed_at":"2026-08-11T10:57:22.102227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.066536Z","title":"Estimation of remaining useful life based on switching kalman filter neural network ensemble","venue":null,"work_id":"d7036d67-bda9-4d8d-b3bc-3db077bb94b0","year":2014},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.277057Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:79722ee8f07dde5b93a667cae5c5ceedf7862500b683d194fb144e655a976308","observation_id":"99217593-7a42-4598-b4e0-3da96b17d8c2","resolution":{"observed_at":"2026-08-11T10:57:22.071615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.853488Z","title":"The existing algorithms in the literature for RUL estimation are either based on multivariate time series analysis or damage progression analysis [3, 18, 19, 20, 26]","venue":null,"work_id":"8dd0b618-605a-4044-be65-aef1c3446ddf","year":2024},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.043528Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:f272873df1b46b32ced6c5f87b271c424f6bd1dd3505339b7738ba88476749df","observation_id":"9590a0fd-97b3-4c99-a147-5ccee5a2ae8a","resolution":{"observed_at":"2026-08-11T10:57:22.864359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:22.023133Z","title":"Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset","venue":null,"work_id":"f2a5af50-b22b-436c-bdff-f98f118d7502","year":2014},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.291901Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:0eaae738006c8c25c32b9a7ff2d8c256eb6c73b791c09083b5ffece2439b9bfd","observation_id":"006b4f62-46ae-486f-96d2-6ce6addb3736","resolution":{"observed_at":"2026-08-11T10:57:22.037394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.981960Z","title":"Applying LSTM to time series predictable through time-window approaches,","venue":null,"work_id":"621aea36-632a-4a93-ac71-75dcc8456105","year":2001},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.298417Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:a39614bed53a32399b2e4fd7bc2d88817711a8385486918ddc939a990adff0ad","observation_id":"c27c0e3e-0309-4e77-ad00-bb22fde9c250","resolution":{"observed_at":"2026-08-11T10:57:21.992922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.941369Z","title":"An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring,","venue":null,"work_id":"57f3bc48-62d2-4494-893c-a779cc6d616a","year":2012},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.312959Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:46dfbac7257209937b09461c6ad8e9dce0191d9110a84be96be126665d326d55","observation_id":"a2b4dd44-e56b-48e8-9c7a-1ef0e152b0d5","resolution":{"observed_at":"2026-08-11T10:57:21.955800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.910650Z","title":"Long short-term memory,","venue":null,"work_id":"6ec723ba-c7f0-47db-bac2-f217401137a2","year":1997},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.321397Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:c99b3f47a46682462e2c79ce01eda24499728416af1aa311ff421f68ab42df8c","observation_id":"7beb19a7-b28c-4d22-9dfd-7b8da7aeebba","resolution":{"observed_at":"2026-08-11T10:57:21.919913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.843417Z","title":"Recurrent neural networks and robust time series prediction","venue":null,"work_id":"f3b2fa5e-1f65-4287-a6ce-81ca71267a79","year":1994},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.336053Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:f1fe40af393f75887d19ffac38599d9d08a2ab0457f475eacd6cd1d8fea4d005","observation_id":"9a132814-dfc1-4a89-ab29-988fd1e18600","resolution":{"observed_at":"2026-08-11T10:57:21.850876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.818633Z","title":"Damage propagation modeling for aircraft engine run-to-failure simulation","venue":null,"work_id":"540e0e8e-0346-4430-87d2-524ce71ca7a6","year":2008},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.341480Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:536d814aef8b65ca9335b13864ec69f0837536c7cc33d334ec8663645297e952","observation_id":"d866294f-adb1-420d-b9a9-dbc15bbd2dc3","resolution":{"observed_at":"2026-08-11T10:57:21.825274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.789943Z","title":"Deep convolutional neural networks on multichannel time series for human activity recognition","venue":null,"work_id":"38c80e39-c952-4a8c-985f-701523457454","year":2015},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.351303Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:5b721d07fc495f9db820fe07994f87a982121daa4b68d82790a7fe46a9fb0466","observation_id":"bd1b2bb8-d90b-4b12-bcbd-f7601b42afb1","resolution":{"observed_at":"2026-08-11T10:57:21.800461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.763798Z","title":"Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance","venue":null,"work_id":"9ddf5cb0-83c2-4475-a196-583d818d1895","year":2021},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.356652Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:1eaa5762acee2efe29003bdc3de54fc6f60081e2b379d4a1750ec5948db66d6f","observation_id":"2b71728e-f0d8-476d-9cf4-dc7f60bc3ddf","resolution":{"observed_at":"2026-08-11T10:57:21.770912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.718112Z","title":"Fault Detection and Predictive Maintenance of Electrical Machines","venue":null,"work_id":"59ba1d56-02fe-46cd-88d1-036d3a21eeb0","year":2022},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.361471Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:ca2dc7be9d11d18dd5466254b7e58563851f8bbd01543eda79d3a6ce65145d28","observation_id":"ed9842f5-1823-48ba-8c0e-74f09dd8b71e","resolution":{"observed_at":"2026-08-11T10:57:21.736846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.679763Z","title":"Machine learning for predictive maintenance: A multiple classifier approach","venue":null,"work_id":"d9fdb028-d30a-48a2-8fc9-21721006a177","year":2014},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.366356Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:fe3d12f3e6ad9deb96f6ded314859814b7e4472d56d0af8f8cbc5afab1d9d480","observation_id":"443622a8-31ef-42c6-93b1-174f6286d3aa","resolution":{"observed_at":"2026-08-11T10:57:21.690572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.648525Z","title":"Turbofan Engine Degradation Simulation Data Set","venue":null,"work_id":"f7cada00-c84f-41dd-8cfc-4a13d3430cd1","year":2008},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.373400Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:5196da8fffd5a450b9eaa2bf80355b1995b3b21e3ea3522b0aa03e6fe052d303","observation_id":"bdac918b-1964-44a4-a9a8-72a29a52dc1f","resolution":{"observed_at":"2026-08-11T10:57:21.659531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.586150Z","title":"Remaining useful life prediction using multi-scale deep convolutional neural network","venue":null,"work_id":"e4e778b2-4890-4db3-9f07-20deca217266","year":2020},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.388655Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:a09955159690c627b65dbed432f75ee4e0e1fc3fbc6cf6cae61e48ecc052d414","observation_id":"95ae44d5-8966-40b5-9b26-9f79e122d95b","resolution":{"observed_at":"2026-08-11T10:57:21.623272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.563637Z","title":"Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks","venue":null,"work_id":"6bb0f297-b553-4f0f-9c53-12f3a53f1537","year":2021},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.395331Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:46194830c7b0c06e88429101fb4418fc1983bbafb53d417a1f12fa8bd64ed27e","observation_id":"91b10bdf-9249-471f-99c2-1325f19665d6","resolution":{"observed_at":"2026-08-11T10:57:21.568716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T10:57:21.534096Z","title":"Evaluation of neural networks in the subject of prognostics as compared to linear regression model","venue":null,"work_id":"e3374e1e-a77e-4884-941f-43528d70a3a6","year":2010},"citing_paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T10:57:21.416884Z"},"links":{"citing_paper":"/paper/2412.15998"},"observation_digest":"sha256:c7b8a2af29a5525a8cb611c785dae8dfe0b41aa04c200efac68166436d0fef12","observation_id":"e977e685-abad-4919-9928-7c12fc30773d","resolution":{"observed_at":"2026-08-11T10:57:21.545081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.15998","last_updated":"2024-12-20T15:48:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T05:45:32.030551Z","submitted_at":"2024-12-20T15:48:57Z","title":"CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":42},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2412.15998."}