{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7T3LGVT4C3SYNUNQZJNP5QOZST","short_pith_number":"pith:7T3LGVT4","schema_version":"1.0","canonical_sha256":"fcf6b3567c16e586d1b0ca5afec1d994f55071a1a3804aa1a3080ec7422be04b","source":{"kind":"arxiv","id":"2005.05239","version":1},"attestation_state":"computed","paper":{"title":"System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Artur Dubrawski, Kyle Miller","submitted_at":"2020-05-11T16:30:54Z","abstract_excerpt":"This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for syst"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2005.05239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2020-05-11T16:30:54Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"535d3a57c44d958219d85240c4351871d62c720572114c12b0bf63f5b3ec0a71","abstract_canon_sha256":"1a27b7f94156fcef0f30fc97714728b091106955f696ea4bf160bf038c51aae7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:01:58.384847Z","signature_b64":"qHg7PELyCgxCZ+s/zoJCI21myQ5/9g/J9+95A5TG4TKpE21W9fFm3l5E9x/PCR3GOpOBeZjOwTko1qILJ57CBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcf6b3567c16e586d1b0ca5afec1d994f55071a1a3804aa1a3080ec7422be04b","last_reissued_at":"2026-07-05T01:01:58.384459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:01:58.384459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Artur Dubrawski, Kyle Miller","submitted_at":"2020-05-11T16:30:54Z","abstract_excerpt":"This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for syst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.05239","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.05239/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.05239","created_at":"2026-07-05T01:01:58.384518+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.05239v1","created_at":"2026-07-05T01:01:58.384518+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.05239","created_at":"2026-07-05T01:01:58.384518+00:00"},{"alias_kind":"pith_short_12","alias_value":"7T3LGVT4C3SY","created_at":"2026-07-05T01:01:58.384518+00:00"},{"alias_kind":"pith_short_16","alias_value":"7T3LGVT4C3SYNUNQ","created_at":"2026-07-05T01:01:58.384518+00:00"},{"alias_kind":"pith_short_8","alias_value":"7T3LGVT4","created_at":"2026-07-05T01:01:58.384518+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.20090","citing_title":"A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST","json":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST.json","graph_json":"https://pith.science/api/pith-number/7T3LGVT4C3SYNUNQZJNP5QOZST/graph.json","events_json":"https://pith.science/api/pith-number/7T3LGVT4C3SYNUNQZJNP5QOZST/events.json","paper":"https://pith.science/paper/7T3LGVT4"},"agent_actions":{"view_html":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST","download_json":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST.json","view_paper":"https://pith.science/paper/7T3LGVT4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.05239&json=true","fetch_graph":"https://pith.science/api/pith-number/7T3LGVT4C3SYNUNQZJNP5QOZST/graph.json","fetch_events":"https://pith.science/api/pith-number/7T3LGVT4C3SYNUNQZJNP5QOZST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST/action/storage_attestation","attest_author":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST/action/author_attestation","sign_citation":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST/action/citation_signature","submit_replication":"https://pith.science/pith/7T3LGVT4C3SYNUNQZJNP5QOZST/action/replication_record"}},"created_at":"2026-07-05T01:01:58.384518+00:00","updated_at":"2026-07-05T01:01:58.384518+00:00"}