{"as_of":"2026-08-12T06:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:820981eb436c9efae952e5a0e3fe52b32d6f5811fac9169a44f157e0d3542dfa","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:11:53.227228Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2412.11245/citation-record","integrity":"/paper/2412.11245/integrity","json":"/paper/2412.11245/citation-record.json","paper":"/paper/2412.11245"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:11:54.594103Z","title":null,"venue":null,"work_id":"5e01d30d-ce02-4f7f-a1cb-d9222035164b","year":null},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.920795Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:af001f07e01acb44441e9042d03929a97c7dd3ab2c9ef6e2eb841ccc1f9d003e","observation_id":"70853041-403d-44a7-a7b2-64380dd186a9","resolution":{"observed_at":"2026-08-11T15:11:54.603830Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.572745Z","title":"The CWRU dataset is a prominent benchmark for assessing fault detection techniques","venue":null,"work_id":"4c958084-c72c-4b2a-8828-3619474bfc7a","year":null},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.930592Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:bfaa4a0303ab99ab4e3f12bac23a410e94cb80fad77c75870c9fc53d1169dbc1","observation_id":"ee289fec-907c-4a01-9136-552c99e7b9d4","resolution":{"observed_at":"2026-08-11T15:11:54.579342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.491191Z","title":"It begins with an explanation of the HEMA for feature extraction, followed by a detailed description of the TDA mechanism","venue":null,"work_id":"1640cb48-bb0a-4c3c-96cb-5d1cf7afeec6","year":2005},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.944945Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:e73acff9f5520b889084189ab4cb33f1447635f6fe9cb77965e1e09fad322b30","observation_id":"37343c9e-5909-4f6f-a9a5-2210a039b94e","resolution":{"observed_at":"2026-08-11T15:11:54.500232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.464603Z","title":"Each subsection offers a detailed evaluation of the models' efficiency, dependability, and capability to handle the challenges of bearing fault detection","venue":null,"work_id":"483cd584-04ad-4b1c-8ed5-2d37b74d6503","year":null},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.951743Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:bfa75ee92c03a97c4c3d0f9515c0574de04576e98f1a7bb5b5a036252ae176fe","observation_id":"95812362-3944-4c7d-b033-c55d536f050e","resolution":{"observed_at":"2026-08-11T15:11:54.474046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.438684Z","title":null,"venue":null,"work_id":"1b4ef240-d0c5-45b8-a258-e45f3c4b58b4","year":null},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.959320Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:5a6f931b71df75467a36b6cf2446a19fc25880e8e15d93eb94d60e26859a3297","observation_id":"1f77e05a-1ad2-4741-8ce3-eda899db57dc","resolution":{"observed_at":"2026-08-11T15:11:54.443753Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.269096Z","title":"Fault diagnosis of hydro -turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network,","venue":null,"work_id":"ca762a0e-1fa0-4a03-a30b-365924485108","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.008908Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:9258057769c9ecbfe9d1e3c39e01f4331710195cba61a32ac23b2f0390f24d2d","observation_id":"ce8e9ed2-1046-4f9c-8b95-bfae536ab9f7","resolution":{"observed_at":"2026-08-11T15:11:54.275640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.414615Z","title":"Towards better benchmarking using the CWRU bearing fault dataset,","venue":null,"work_id":"27a23f68-04c7-4877-bbfe-ce5443a2901f","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.967358Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:5025d3f62441fef89409601ba06ed0bbbd18717ba4e3e4339b14f503f6d1a4db","observation_id":"62921e0c-09f5-45af-b922-9e8349495308","resolution":{"observed_at":"2026-08-11T15:11:54.420182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.388243Z","title":"Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review,","venue":null,"work_id":"0f9510db-0afe-494b-bbf8-1edc53f8c3d1","year":2020},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.976803Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:4548672dbe754219612f32d35e37ef8656f3ed2ceba91a31554f43ddc9eef2e8","observation_id":"5286bb38-d10d-4a34-8565-bf8f520b39f5","resolution":{"observed_at":"2026-08-11T15:11:54.399191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.362880Z","title":"Extracting features from time series,","venue":null,"work_id":"86d88f98-c48c-49bc-9205-8a17b65e458c","year":2019},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.984854Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:bfdfca69d7ce0d182f8b4e6a3215e4d263f1b3a9897a0218f43d512b999ef6fb","observation_id":"b7658fb2-abd1-43ed-ab89-ed82c997b3a0","resolution":{"observed_at":"2026-08-11T15:11:54.369497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.339000Z","title":"Time domain synchronous moving average and its application to gear fault detection,","venue":null,"work_id":"f3ca7487-5204-4136-857e-e6f12295cc59","year":2019},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.991517Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:2babbacd0e6c15545f6bc95cc6d92854b637a3e7d53d091d5e62a7242bc87e95","observation_id":"b9ba0471-aaca-4766-a302-e02325d4ed14","resolution":{"observed_at":"2026-08-11T15:11:54.346666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.302088Z","title":"End -to-end CNN+ LSTM deep learning approach for bearing fault diagnosis,","venue":null,"work_id":"513b1488-3e2c-417c-b4ad-0e08750e6ab5","year":2021},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.998136Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:5ad53d332ac53b0109575715b8bef1cb97649593eabe7952065e1003e0bae26f","observation_id":"3ad13927-bc7f-4f7f-a69d-c9c58aebff9d","resolution":{"observed_at":"2026-08-11T15:11:54.309797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.079014Z","title":"A study on the evaluation of tokenizer performance in natural language processing,","venue":null,"work_id":"3680dd8f-c462-4077-83aa-5acfc51c8339","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.058379Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:3bef081f87bf17f4b9bd2d4125396eda25ee69a8568198e98c5aa2ddbfe42e89","observation_id":"fdeb2f3a-9f9b-4641-9ddc-ebc8bbc7deb1","resolution":{"observed_at":"2026-08-11T15:11:54.087219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.236993Z","title":"Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data,","venue":null,"work_id":"1860b5a7-4878-4a85-90e0-480597bd67cc","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.016584Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:122f9983c89ba8358bc281e4c936767103c50688477ba2840d44ba0846fe7765","observation_id":"f671e1dc-665d-4368-924b-d4debeb8cc21","resolution":{"observed_at":"2026-08-11T15:11:54.254254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.205628Z","title":"Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks,","venue":null,"work_id":"3fd85a09-2318-4114-8679-cafe43994b0e","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.023477Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:2ddc9b286e786c9e963e5869757e06b0fe03f97e287bc7898a5fde1ab0b41e39","observation_id":"06dc4dce-de3c-4176-97ee-652b2413a6a3","resolution":{"observed_at":"2026-08-11T15:11:54.211978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.169024Z","title":"Twins transformer: Cross-attention based two- branch transformer network for rotating bearing fault diagnosis,","venue":null,"work_id":"d4b50027-917e-4a53-b717-d1939684d15b","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.036950Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:613d81b4a49f590f393c0174fc5103cda70d60ae684fe440d6f4924744fafff3","observation_id":"0e053468-882a-417f-a7f3-f543216180be","resolution":{"observed_at":"2026-08-11T15:11:54.186133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.135392Z","title":"Compound fault diagnosis for industrial robots based on dual-transformer networks,","venue":null,"work_id":"04322d80-42de-4472-b2aa-f658f3d82909","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.044331Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:834c3f44dc1c6bfaeff731b6aa81b1d3a07d6b2280fa1a45dc586cda716608d6","observation_id":"1b73757d-eef1-4f80-98a1-cc437da0e19d","resolution":{"observed_at":"2026-08-11T15:11:54.143560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.112840Z","title":"Deep learning attention mechanism in medical image analysis: Basics and beyonds,","venue":null,"work_id":"366a7bb5-c805-49ee-b310-ed96ed21a7cd","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.051363Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:6c2ba1eb26341538fd2889613e8bac7936935b7abfbe00ad8f0432a08518e84d","observation_id":"83c746a9-28f4-4f41-8f0f-af9bc893a9b2","resolution":{"observed_at":"2026-08-11T15:11:54.119784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.540710Z","title":null,"venue":null,"work_id":"dcdf8378-70bd-44cb-b5b5-624cca4d5457","year":null},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:52.937950Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:7949c9684a421d962288eb4c4f6cdfa1e81aec6bc10e4a6e0a8ea5bc7117faca","observation_id":"abb89079-e2c6-43aa-9635-9104359c45a7","resolution":{"observed_at":"2026-08-11T15:11:54.553060Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.043488Z","title":"Vision Transformer Based Tokenization for Enhanced Breast Cancer Histopathological Images Classification,","venue":null,"work_id":"c6cab3f2-16d3-483a-bba2-90ed5d6fa48c","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.063967Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:12c3bc62edf16c5a45d911c02d665b6fba72f6af696f8604e211f60255c547be","observation_id":"d3dcd310-be38-4ae3-9a1c-d118a32c8f90","resolution":{"observed_at":"2026-08-11T15:11:54.052329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.11006","last_updated":"2023-09-20T02:20:11Z","snapshot_observed_at":"2026-07-06T16:20:48.868168Z","submitted_at":"2023-09-20T02:20:11Z","title":"STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.11006","snapshot_observed_at":"2026-08-11T15:11:53.070814Z","title":"Starnet: Sensor trustworthiness and anomaly recognition via approximated likelihood regret for robust edge autonomy,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.070814Z"},"links":{"cited_paper":"/paper/2309.11006","citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:40b8f5e6b97c7f728d7bc4a33464536fc9bb2072005ab85b38d97297a9a272b3","observation_id":"89568873-a8fe-45bc-a0d5-4b08fb59e2ba","resolution":{"observed_at":"2026-08-11T15:11:53.070814Z","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":"10.1007/s10203-024-00488-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:11:53.306874Z","title":"Deep prediction on financial market sequence for enhancing economic policies,","venue":null,"work_id":"95928d6a-c2eb-42eb-bd27-abb6b7cc2a1e","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.077039Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:1cbd7a9b866c583866cf8ce84a133cf9c6860add7d62b403f21b7956cecbe0b2","observation_id":"3d8c886f-80ed-41f6-a2dc-ea42ad958746","resolution":{"observed_at":"2026-08-11T15:11:53.315670Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2408.08448","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:11:53.597991Z","title":"Exploring Cross -model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability,","venue":null,"work_id":"b2493f44-587f-4e4d-9987-d4a273455933","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.083559Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:787ada117960cab2b548d8a87b0b8bdf0417b7208909c9c19ddfa7a2dda5b92b","observation_id":"f53e67f8-98ea-4bb5-a639-f3975c8ed295","resolution":{"observed_at":"2026-08-11T15:11:53.614114Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s12206-022-0102-1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:11:53.280761Z","title":"Application of recurrent neural network to mechanical fault diagnosis: a review,","venue":null,"work_id":"827308a2-0a96-4bea-b7ec-b4c832140c08","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.091017Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:a57e2733580c8dc75ffb6a2e344b8e9cd54491327c7a6fa40ab85a804b3bbbf4","observation_id":"95086ceb-1b0f-4d5f-88d1-8cc8a09e6465","resolution":{"observed_at":"2026-08-11T15:11:53.290058Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:54.022127Z","title":"Fault diagnosis of rotating machinery based on recurrent neural networks,","venue":null,"work_id":"ae4de748-24e8-4a4b-81dc-8fad0f9cab6e","year":2021},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.100977Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:3aadab8d628a29f01bd90a48e922bac3a487d9fd7f9817167937a529bb9205fc","observation_id":"38532aa0-cb0c-4ba5-8e35-196adbe3f991","resolution":{"observed_at":"2026-08-11T15:11:54.028280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.993240Z","title":"A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems,","venue":null,"work_id":"d3fd9aed-d05c-4699-ad7d-7ac8be84aa4f","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.110518Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:85fda922323c166655514f4d1db3ac367319753e4e93f15124aaccc90239536a","observation_id":"88bd1bc0-1947-4dfd-9fa6-d8f14ca4bb67","resolution":{"observed_at":"2026-08-11T15:11:54.006783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.964915Z","title":"A power transformer fault prediction method through temporal convolutional network on dissolved gas chromatography data,","venue":null,"work_id":"8d24cc05-5a17-4368-a516-e5261dc2e8d9","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.126647Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:cca39b02e78ef31d94235721e0f848c9981cb35e616e403eabe38290da6999c2","observation_id":"8f779465-1df1-43d8-8275-ead18098f3d1","resolution":{"observed_at":"2026-08-11T15:11:53.971984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.934520Z","title":"An innovative transformer neural network for fault detection and classification for photovoltaic modules,","venue":null,"work_id":"5b4981a7-63a7-41fd-ae3f-3ad3eb709ac3","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.134934Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:7989920fabe5777d1d0e9166e9ea3705d2d147dab7cccb90a5cbf7418be1c9dd","observation_id":"e36258a6-9bb4-4f98-ae67-701328bb2cb5","resolution":{"observed_at":"2026-08-11T15:11:53.941420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.905819Z","title":"Variational attention-based interpretable transformer network for rotary machine fault diagnosis,","venue":null,"work_id":"23e5a6ea-eecd-42fe-9752-569eeb1ae953","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.143024Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:0cbf24f641c1fc60f27f5e8ba948ccb7cecab90d8aec38134d3f65fa65d8ffb2","observation_id":"953a80ef-7939-428f-aa6d-ef6830788d1a","resolution":{"observed_at":"2026-08-11T15:11:53.915705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.881806Z","title":"CNN -based transformer model for fault detection in power system networks,","venue":null,"work_id":"989f2997-7293-42b9-9c68-a8b1d8b5013b","year":2023},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.151227Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:3310bb7566c15b05fed7cbff17d4c3dbb772447695cf24bd1f15f5ecf481c570","observation_id":"f058a33f-d30e-493b-9bb0-19ea480955b9","resolution":{"observed_at":"2026-08-11T15:11:53.892323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.847286Z","title":"A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model,","venue":null,"work_id":"0bddc00b-e9b6-4988-ae46-4823e162789b","year":2022},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.161107Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:f16dd4c873a9161b1a0736d3570e2867f2639a8f2e13ad0830cd302a258b5199","observation_id":"003f58b6-866a-4b81-a021-3201aa8b7e9f","resolution":{"observed_at":"2026-08-11T15:11:53.860783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:11:53.169829Z","title":"Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.169829Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:b5525583765cc4cacdac61ee34e47279e15e44812a976981ae41d97bbaec9445","observation_id":"a6c23fa3-d130-4db7-b052-4f67199ad907","resolution":{"observed_at":"2026-08-11T15:11:53.169829Z","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":"2026-08-11T15:11:53.811484Z","title":"Machine learning based bearing fault diagnosis using the case western reserve university data: A review,","venue":null,"work_id":"45f792ec-9457-488a-bc7a-34c17f0d997b","year":2021},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.176608Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:2fbfa0d5968ad14e3852a9042ff753a119fe05557b8b4fc68dff4128e68f52b0","observation_id":"373e42b4-14b5-4c4b-900b-a898cf7679ae","resolution":{"observed_at":"2026-08-11T15:11:53.820179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.785249Z","title":"Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset,","venue":null,"work_id":"c8a92b39-ce93-4345-888e-3f684481c4cd","year":2024},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.186530Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:c0a4da03cc884ecd0bf1e8325b4a178e3b108804ccf81613d1f8f7873d55a1df","observation_id":"a4cd0fe8-9dbb-42c8-a0b9-01cefe5080ee","resolution":{"observed_at":"2026-08-11T15:11:53.792594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.758086Z","title":"Hull, Active Investing","venue":null,"work_id":"df37c664-7a49-4f3e-b66f-8cdaf017fe2b","year":2001},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.193283Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:3b85f8148bf536eeec25bf6324a9d97be5109ed1d84f7efcbaf4642a287efbcd","observation_id":"39c6c3dc-5562-4469-bdf6-a5d1539b015e","resolution":{"observed_at":"2026-08-11T15:11:53.769847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.729525Z","title":"Moving convolutional neural networks to embedded systems: the alexnet and VGG -16 case,","venue":null,"work_id":"918549dd-7203-4b87-a2ec-1674d834eb3b","year":2018},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.203404Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:81c3b0ee3fe88ba0316857e8bd4cbc63d7dbc2730ff8828506204805d7af65ae","observation_id":"51a749a6-add0-4b99-9ddc-01a61d6a5673","resolution":{"observed_at":"2026-08-11T15:11:53.736141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.701900Z","title":"A feature transferring fault diagnosis based on WPDR, FSWT and GoogLeNet,","venue":null,"work_id":"9b4f4851-f97e-453f-81f7-bb7a944cef6e","year":2020},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.210880Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:3c8763c2a70f604b7ee640ca27329e9683fd1bbde475f233800f496dba5e275e","observation_id":"8febe963-49e1-49e9-8ed7-d2638ba30ec3","resolution":{"observed_at":"2026-08-11T15:11:53.709223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.666671Z","title":"A transfer convolutional neural network for fault diagnosis based on ResNet-50,","venue":null,"work_id":"3568e79d-b8d3-40af-928b-84535cf153ff","year":2020},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.219814Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:1dc5e1f0e7a9d47bca4b98c4319e9c160dad24782547af3059b34b73c086c226","observation_id":"e8867093-bc2d-424a-a19e-a77f71fefca1","resolution":{"observed_at":"2026-08-11T15:11:53.673080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T15:11:53.636346Z","title":"Motor fault diagnosis algorithm based on wavelet and attention mechanism,","venue":null,"work_id":"aed44bed-651e-467f-9e02-d2664896f3b0","year":2021},"citing_paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T15:11:53.227228Z"},"links":{"citing_paper":"/paper/2412.11245"},"observation_digest":"sha256:567254f7d5519ec2ab39e590ba978e499431c120d0999a910cd8cea7459e211f","observation_id":"21fdbe3c-f52d-40cd-84f6-910445b1dfc6","resolution":{"observed_at":"2026-08-11T15:11:53.646251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11245","last_updated":"2024-12-15T16:51:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T19:39:57.991889Z","submitted_at":"2024-12-15T16:51:31Z","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":3,"verified_fuzzy":30},"total_outbound_references":38},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.11245."}