{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HGTODOECVMTKNEY3WJA45GGKYL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a0e3931f2126392dce571025b366f7b4eb74dfc79bed5107b2763add147a8f35","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-17T02:07:23Z","title_canon_sha256":"d6c96455d074c36a9b0caeaa472bb18f6429a91fb7d77ef14e698b86763f9183"},"schema_version":"1.0","source":{"id":"1901.02057","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02057","created_at":"2026-07-05T04:52:21Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02057v2","created_at":"2026-07-05T04:52:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02057","created_at":"2026-07-05T04:52:21Z"},{"alias_kind":"pith_short_12","alias_value":"HGTODOECVMTK","created_at":"2026-07-05T04:52:21Z"},{"alias_kind":"pith_short_16","alias_value":"HGTODOECVMTKNEY3","created_at":"2026-07-05T04:52:21Z"},{"alias_kind":"pith_short_8","alias_value":"HGTODOEC","created_at":"2026-07-05T04:52:21Z"}],"graph_snapshots":[{"event_id":"sha256:352476728e48c4ba16c73831f520f16b8b6fc909e249d18fc6e884716161849b","target":"graph","created_at":"2026-07-05T04:52:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1901.02057/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict fa","authors_text":"Beitong Zhou, Cheng cheng, Guijun Ma, Hai-Tao Zhang, Han Ding, Huan Zhao, Ye Yuan","cross_cats":["eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-17T02:07:23Z","title":"A General End-to-end Diagnosis Framework for Manufacturing Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02057","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a37621f5761e67caf57ec4426cc6bdc2992d88e21fc52c91ff089ec56763f8fc","target":"record","created_at":"2026-07-05T04:52:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a0e3931f2126392dce571025b366f7b4eb74dfc79bed5107b2763add147a8f35","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-17T02:07:23Z","title_canon_sha256":"d6c96455d074c36a9b0caeaa472bb18f6429a91fb7d77ef14e698b86763f9183"},"schema_version":"1.0","source":{"id":"1901.02057","kind":"arxiv","version":2}},"canonical_sha256":"39a6e1b882ab26a6931bb241ce98cac2cbdfc5a9cf8c22a9eff71ea846e8fc91","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39a6e1b882ab26a6931bb241ce98cac2cbdfc5a9cf8c22a9eff71ea846e8fc91","first_computed_at":"2026-07-05T04:52:21.782966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:52:21.782966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SP0RSh57kMscYzELoUHzepgfxbJSsdyqoXA2F8+rTFwe66AImEZ8Mk7P/8zUkepZ0BxzRveWs9lMBNqeaDQEBA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:52:21.783498Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.02057","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a37621f5761e67caf57ec4426cc6bdc2992d88e21fc52c91ff089ec56763f8fc","sha256:352476728e48c4ba16c73831f520f16b8b6fc909e249d18fc6e884716161849b"],"state_sha256":"1fb18966ee4f46ac32be02dc9c02658b402c64117b3536f6f960e7120f4df0e1"}