{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CEXWSACRKXIDQLENDQX5C5PADY","short_pith_number":"pith:CEXWSACR","schema_version":"1.0","canonical_sha256":"112f69005155d0382c8d1c2fd175e01e35fa70e522dab8654504ee73d00e36c0","source":{"kind":"arxiv","id":"1904.00575","version":1},"attestation_state":"computed","paper":{"title":"A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Beitong Zhou, Cheng cheng, Guijun Ma, Wenqian Jiang, Ye Yuan","submitted_at":"2019-04-01T06:11:44Z","abstract_excerpt":"This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. I"},"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":"1904.00575","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-01T06:11:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dc57114be4731d3a8d1b4a8eb467a1b4ad0736639ee0afed566defb0d0479577","abstract_canon_sha256":"cc5ac35d52ee09cbce9d275d4be491f8cfa9474015e2ff90aa8c3b44d73f447a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:32:08.784197Z","signature_b64":"JsqHYghn9D5fu7ocN/x4HLp1UIsmRczbtCNuHMJcXPoLvZtEzIrN9lY50c8AU1WiyMRQpEb0S+eYPRr0Fm9NDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"112f69005155d0382c8d1c2fd175e01e35fa70e522dab8654504ee73d00e36c0","last_reissued_at":"2026-07-05T04:32:08.783744Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:32:08.783744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Beitong Zhou, Cheng cheng, Guijun Ma, Wenqian Jiang, Ye Yuan","submitted_at":"2019-04-01T06:11:44Z","abstract_excerpt":"This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00575","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/1904.00575/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":"1904.00575","created_at":"2026-07-05T04:32:08.783806+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00575v1","created_at":"2026-07-05T04:32:08.783806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00575","created_at":"2026-07-05T04:32:08.783806+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEXWSACRKXID","created_at":"2026-07-05T04:32:08.783806+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEXWSACRKXIDQLEN","created_at":"2026-07-05T04:32:08.783806+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEXWSACR","created_at":"2026-07-05T04:32:08.783806+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/CEXWSACRKXIDQLENDQX5C5PADY","json":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY.json","graph_json":"https://pith.science/api/pith-number/CEXWSACRKXIDQLENDQX5C5PADY/graph.json","events_json":"https://pith.science/api/pith-number/CEXWSACRKXIDQLENDQX5C5PADY/events.json","paper":"https://pith.science/paper/CEXWSACR"},"agent_actions":{"view_html":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY","download_json":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY.json","view_paper":"https://pith.science/paper/CEXWSACR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00575&json=true","fetch_graph":"https://pith.science/api/pith-number/CEXWSACRKXIDQLENDQX5C5PADY/graph.json","fetch_events":"https://pith.science/api/pith-number/CEXWSACRKXIDQLENDQX5C5PADY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY/action/storage_attestation","attest_author":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY/action/author_attestation","sign_citation":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY/action/citation_signature","submit_replication":"https://pith.science/pith/CEXWSACRKXIDQLENDQX5C5PADY/action/replication_record"}},"created_at":"2026-07-05T04:32:08.783806+00:00","updated_at":"2026-07-05T04:32:08.783806+00:00"}