{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XAT76WZZDNDUZ43V3SQKLR5RW6","short_pith_number":"pith:XAT76WZZ","schema_version":"1.0","canonical_sha256":"b827ff5b391b474cf375dca0a5c7b1b78667f8e6b64c677d4f7a464ffc5181c5","source":{"kind":"arxiv","id":"1904.01128","version":1},"attestation_state":"computed","paper":{"title":"Analysis of Large Heterogeneous Repairable System Reliability Data with Static System Attributes and Dynamic Sensor Measurement in Big Data Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Rong Pan, Xiao Liu","submitted_at":"2019-04-01T22:13:27Z","abstract_excerpt":"In Big Data environment, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates. In addition to static covariates, which include time-invariant system attributes such as nominal operating conditions, geo-locations, etc., the recent advances of sensing technologies have also made it possible to obtain dynamic sensor measurement of system operating and environmental conditions. As a common practice in the Big Data environment, the massive reliability data are typically stored in some distributed storage sys"},"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":"1904.01128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2019-04-01T22:13:27Z","cross_cats_sorted":[],"title_canon_sha256":"44c9815ae91e2fcaf3c5ebf0c22c14d633fdd5cc3fbb6663e8402c480248061c","abstract_canon_sha256":"0ba16d49f8ca9c6904a2bec43e9577cfdd957b74a18db364beb17835c9427cdb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:35.845806Z","signature_b64":"3XTlWeS8P5fRGnwFrSsJ8FAK3D8ZxE+AV/I7swcUn1b3i36oS6uIjTgqPC8cv4+TmtXEeJzATUaznKpNGB8ZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b827ff5b391b474cf375dca0a5c7b1b78667f8e6b64c677d4f7a464ffc5181c5","last_reissued_at":"2026-05-17T23:49:35.845068Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:35.845068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis of Large Heterogeneous Repairable System Reliability Data with Static System Attributes and Dynamic Sensor Measurement in Big Data Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Rong Pan, Xiao Liu","submitted_at":"2019-04-01T22:13:27Z","abstract_excerpt":"In Big Data environment, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates. In addition to static covariates, which include time-invariant system attributes such as nominal operating conditions, geo-locations, etc., the recent advances of sensing technologies have also made it possible to obtain dynamic sensor measurement of system operating and environmental conditions. As a common practice in the Big Data environment, the massive reliability data are typically stored in some distributed storage sys"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01128","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":""},"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":"1904.01128","created_at":"2026-05-17T23:49:35.845175+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.01128v1","created_at":"2026-05-17T23:49:35.845175+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01128","created_at":"2026-05-17T23:49:35.845175+00:00"},{"alias_kind":"pith_short_12","alias_value":"XAT76WZZDNDU","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XAT76WZZDNDUZ43V","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XAT76WZZ","created_at":"2026-05-18T12:33:33.725879+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/XAT76WZZDNDUZ43V3SQKLR5RW6","json":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6.json","graph_json":"https://pith.science/api/pith-number/XAT76WZZDNDUZ43V3SQKLR5RW6/graph.json","events_json":"https://pith.science/api/pith-number/XAT76WZZDNDUZ43V3SQKLR5RW6/events.json","paper":"https://pith.science/paper/XAT76WZZ"},"agent_actions":{"view_html":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6","download_json":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6.json","view_paper":"https://pith.science/paper/XAT76WZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.01128&json=true","fetch_graph":"https://pith.science/api/pith-number/XAT76WZZDNDUZ43V3SQKLR5RW6/graph.json","fetch_events":"https://pith.science/api/pith-number/XAT76WZZDNDUZ43V3SQKLR5RW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6/action/storage_attestation","attest_author":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6/action/author_attestation","sign_citation":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6/action/citation_signature","submit_replication":"https://pith.science/pith/XAT76WZZDNDUZ43V3SQKLR5RW6/action/replication_record"}},"created_at":"2026-05-17T23:49:35.845175+00:00","updated_at":"2026-05-17T23:49:35.845175+00:00"}