{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YIXOAPSEMXVI6LIQYBYLGXQ5MQ","short_pith_number":"pith:YIXOAPSE","schema_version":"1.0","canonical_sha256":"c22ee03e4465ea8f2d10c070b35e1d643b9d9b5143315311c5b69e3289ab08e7","source":{"kind":"arxiv","id":"2607.13368","version":1},"attestation_state":"computed","paper":{"title":"Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Dongjin Lee, Youngjae Jeon","submitted_at":"2026-07-15T01:10:25Z","abstract_excerpt":"Reliable fault diagnosis of rotating machinery is essential for the safe and stable operation of industrial systems. Although deep learning methods perform well under closed-set conditions, real machinery may encounter previously unseen fault states. Existing open-set fault diagnosis (OSFD) methods remain limited in fine-grained severity diagnosis because they often rely on coarse type levels, heuristically selected Short-Time Fourier Transform (STFT) settings, and global class boundaries. We propose a fine-grained OSFD method that combines metric-guided data-centric (MGDC) STFT configuration "},"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":"2607.13368","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2026-07-15T01:10:25Z","cross_cats_sorted":[],"title_canon_sha256":"bd03ea5575ab19f84ef37b2186756b2626cbcc2941712524519eb575f59967fe","abstract_canon_sha256":"f9df073cffc94f1bf83bf1220ea4988edf56b3dda51032b5ab7f0a96a73cd106"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T00:22:14.334286Z","signature_b64":"Z9lcfUFFaNhGHorOjkc9MJ1J3wFGWsGoWeWUJjOsCY9GF5GQ/Zk1jwZTQEYMTzqzvGx8fBn4phja0gGk23OxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c22ee03e4465ea8f2d10c070b35e1d643b9d9b5143315311c5b69e3289ab08e7","last_reissued_at":"2026-07-16T00:22:14.333415Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T00:22:14.333415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Dongjin Lee, Youngjae Jeon","submitted_at":"2026-07-15T01:10:25Z","abstract_excerpt":"Reliable fault diagnosis of rotating machinery is essential for the safe and stable operation of industrial systems. Although deep learning methods perform well under closed-set conditions, real machinery may encounter previously unseen fault states. Existing open-set fault diagnosis (OSFD) methods remain limited in fine-grained severity diagnosis because they often rely on coarse type levels, heuristically selected Short-Time Fourier Transform (STFT) settings, and global class boundaries. We propose a fine-grained OSFD method that combines metric-guided data-centric (MGDC) STFT configuration "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13368","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/2607.13368/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":"2607.13368","created_at":"2026-07-16T00:22:14.333877+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13368v1","created_at":"2026-07-16T00:22:14.333877+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13368","created_at":"2026-07-16T00:22:14.333877+00:00"},{"alias_kind":"pith_short_12","alias_value":"YIXOAPSEMXVI","created_at":"2026-07-16T00:22:14.333877+00:00"},{"alias_kind":"pith_short_16","alias_value":"YIXOAPSEMXVI6LIQ","created_at":"2026-07-16T00:22:14.333877+00:00"},{"alias_kind":"pith_short_8","alias_value":"YIXOAPSE","created_at":"2026-07-16T00:22:14.333877+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/YIXOAPSEMXVI6LIQYBYLGXQ5MQ","json":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ.json","graph_json":"https://pith.science/api/pith-number/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/graph.json","events_json":"https://pith.science/api/pith-number/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/events.json","paper":"https://pith.science/paper/YIXOAPSE"},"agent_actions":{"view_html":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ","download_json":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ.json","view_paper":"https://pith.science/paper/YIXOAPSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13368&json=true","fetch_graph":"https://pith.science/api/pith-number/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/graph.json","fetch_events":"https://pith.science/api/pith-number/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/action/storage_attestation","attest_author":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/action/author_attestation","sign_citation":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/action/citation_signature","submit_replication":"https://pith.science/pith/YIXOAPSEMXVI6LIQYBYLGXQ5MQ/action/replication_record"}},"created_at":"2026-07-16T00:22:14.333877+00:00","updated_at":"2026-07-16T00:22:14.333877+00:00"}