{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:56P2N37MJC4P76ZIBLUIEQZ3ZX","short_pith_number":"pith:56P2N37M","schema_version":"1.0","canonical_sha256":"ef9fa6efec48b8fffb280ae882433bcdf4f5566ecb5691ab8913a61f0dac2e70","source":{"kind":"arxiv","id":"2411.15185","version":1},"attestation_state":"computed","paper":{"title":"Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Heng Luo, Tian Niu, Zijun Xu, Ziqing Zhou","submitted_at":"2024-11-19T03:00:02Z","abstract_excerpt":"The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many models still face interpretability and compelling uncertainty modeling challenges. This paper introduces a modified Gaussian Process Regression (GPR) model for RUL interval prediction, tailored for the complexities of manufacturing process development. The modified GPR predicts confidence intervals by learning from historical data and addresses uncertainty modeling in a more structured way. The approa"},"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":"2411.15185","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-19T03:00:02Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"e278a6491fd9f1a54c6a5989474faebe6c6021d2b66e19e97ca965100e3d8cf7","abstract_canon_sha256":"5247c1778294b93e98ca86d8694f7a9f0421ed90603a483043cf1ab81b7bd3d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:30.987635Z","signature_b64":"nO0KeURDnLweJBt7YukShU5G1lZ9mzibuvsCic5n0lXoJhIeqWcT2l/fN7U5w+VwMOFx8azHJj+TTau2mneKAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef9fa6efec48b8fffb280ae882433bcdf4f5566ecb5691ab8913a61f0dac2e70","last_reissued_at":"2026-07-05T09:39:30.987148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:30.987148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Heng Luo, Tian Niu, Zijun Xu, Ziqing Zhou","submitted_at":"2024-11-19T03:00:02Z","abstract_excerpt":"The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many models still face interpretability and compelling uncertainty modeling challenges. This paper introduces a modified Gaussian Process Regression (GPR) model for RUL interval prediction, tailored for the complexities of manufacturing process development. The modified GPR predicts confidence intervals by learning from historical data and addresses uncertainty modeling in a more structured way. The approa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15185","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/2411.15185/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":"2411.15185","created_at":"2026-07-05T09:39:30.987204+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15185v1","created_at":"2026-07-05T09:39:30.987204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15185","created_at":"2026-07-05T09:39:30.987204+00:00"},{"alias_kind":"pith_short_12","alias_value":"56P2N37MJC4P","created_at":"2026-07-05T09:39:30.987204+00:00"},{"alias_kind":"pith_short_16","alias_value":"56P2N37MJC4P76ZI","created_at":"2026-07-05T09:39:30.987204+00:00"},{"alias_kind":"pith_short_8","alias_value":"56P2N37M","created_at":"2026-07-05T09:39:30.987204+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX","json":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX.json","graph_json":"https://pith.science/api/pith-number/56P2N37MJC4P76ZIBLUIEQZ3ZX/graph.json","events_json":"https://pith.science/api/pith-number/56P2N37MJC4P76ZIBLUIEQZ3ZX/events.json","paper":"https://pith.science/paper/56P2N37M"},"agent_actions":{"view_html":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX","download_json":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX.json","view_paper":"https://pith.science/paper/56P2N37M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15185&json=true","fetch_graph":"https://pith.science/api/pith-number/56P2N37MJC4P76ZIBLUIEQZ3ZX/graph.json","fetch_events":"https://pith.science/api/pith-number/56P2N37MJC4P76ZIBLUIEQZ3ZX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX/action/storage_attestation","attest_author":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX/action/author_attestation","sign_citation":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX/action/citation_signature","submit_replication":"https://pith.science/pith/56P2N37MJC4P76ZIBLUIEQZ3ZX/action/replication_record"}},"created_at":"2026-07-05T09:39:30.987204+00:00","updated_at":"2026-07-05T09:39:30.987204+00:00"}