{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TOM3CBHH3N23F5BBUDH34NKQK5","short_pith_number":"pith:TOM3CBHH","schema_version":"1.0","canonical_sha256":"9b99b104e7db75b2f421a0cfbe355057499c4fb38373ad115535853aba5d757c","source":{"kind":"arxiv","id":"2507.07298","version":1},"attestation_state":"computed","paper":{"title":"Multilayer GNN for Predictive Maintenance and Clustering in Power Grids","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Chau Le, Harun Pirim, Muhammad Kazim, Om Prakash Yadav, Trung Le","submitted_at":"2025-07-09T21:44:51Z","abstract_excerpt":"Unplanned power outages cost the US economy over $150 billion annually, partly due to predictive maintenance (PdM) models that overlook spatial, temporal, and causal dependencies in grid failures. This study introduces a multilayer Graph Neural Network (GNN) framework to enhance PdM and enable resilience-based substation clustering. Using seven years of incident data from Oklahoma Gas & Electric (292,830 records across 347 substations), the framework integrates Graph Attention Networks (spatial), Graph Convolutional Networks (temporal), and Graph Isomorphism Networks (causal), fused through at"},"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":"2507.07298","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-07-09T21:44:51Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"08711605fd174652f330d0c527a5b44f5ee608acc3992f408bb9d744f061fd6e","abstract_canon_sha256":"26227304a703953db24d916cb9a325eb86970fe52ced9cc33d47c053922c60b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:55.358387Z","signature_b64":"PAj61Wg07uKbXGfzHoxj9Wb/h4eY/3kUDLy3+JfOqVhamPY/FpmapLq5ap7qSaKOodsBjcSYF8Gi4PdhYtS7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b99b104e7db75b2f421a0cfbe355057499c4fb38373ad115535853aba5d757c","last_reissued_at":"2026-07-05T11:34:55.357883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:55.357883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multilayer GNN for Predictive Maintenance and Clustering in Power Grids","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Chau Le, Harun Pirim, Muhammad Kazim, Om Prakash Yadav, Trung Le","submitted_at":"2025-07-09T21:44:51Z","abstract_excerpt":"Unplanned power outages cost the US economy over $150 billion annually, partly due to predictive maintenance (PdM) models that overlook spatial, temporal, and causal dependencies in grid failures. This study introduces a multilayer Graph Neural Network (GNN) framework to enhance PdM and enable resilience-based substation clustering. Using seven years of incident data from Oklahoma Gas & Electric (292,830 records across 347 substations), the framework integrates Graph Attention Networks (spatial), Graph Convolutional Networks (temporal), and Graph Isomorphism Networks (causal), fused through at"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07298","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/2507.07298/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":"2507.07298","created_at":"2026-07-05T11:34:55.357940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.07298v1","created_at":"2026-07-05T11:34:55.357940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07298","created_at":"2026-07-05T11:34:55.357940+00:00"},{"alias_kind":"pith_short_12","alias_value":"TOM3CBHH3N23","created_at":"2026-07-05T11:34:55.357940+00:00"},{"alias_kind":"pith_short_16","alias_value":"TOM3CBHH3N23F5BB","created_at":"2026-07-05T11:34:55.357940+00:00"},{"alias_kind":"pith_short_8","alias_value":"TOM3CBHH","created_at":"2026-07-05T11:34:55.357940+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/TOM3CBHH3N23F5BBUDH34NKQK5","json":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5.json","graph_json":"https://pith.science/api/pith-number/TOM3CBHH3N23F5BBUDH34NKQK5/graph.json","events_json":"https://pith.science/api/pith-number/TOM3CBHH3N23F5BBUDH34NKQK5/events.json","paper":"https://pith.science/paper/TOM3CBHH"},"agent_actions":{"view_html":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5","download_json":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5.json","view_paper":"https://pith.science/paper/TOM3CBHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.07298&json=true","fetch_graph":"https://pith.science/api/pith-number/TOM3CBHH3N23F5BBUDH34NKQK5/graph.json","fetch_events":"https://pith.science/api/pith-number/TOM3CBHH3N23F5BBUDH34NKQK5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5/action/storage_attestation","attest_author":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5/action/author_attestation","sign_citation":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5/action/citation_signature","submit_replication":"https://pith.science/pith/TOM3CBHH3N23F5BBUDH34NKQK5/action/replication_record"}},"created_at":"2026-07-05T11:34:55.357940+00:00","updated_at":"2026-07-05T11:34:55.357940+00:00"}