{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T02:56:05Z","timestamp":1762052165648,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"The Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["231695"],"award-info":[{"award-number":["231695"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to interpret the effect of aging on the fault causality structure over time. At periodic intervals, the model captures the cause\u2013effect relations in the form of interpretable logic trees, then represents them in one fault tree model that reflects the changes in the fault causality structure over time due to the system aging. The proposed model provides a prognosis of the probability for fault occurrence using a set of extracted causality rules that combine the discovered root causes over time in a bottom-up manner. The well-known NASA turbofan engine dataset is used as an illustrative example of the proposed methodology.<\/jats:p>","DOI":"10.3390\/a15060178","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T22:04:06Z","timestamp":1653429846000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9622-8573","authenticated-orcid":false,"given":"Kerelous","family":"Waghen","sequence":"first","affiliation":[{"name":"Mathematics and Industrial Engineering Department, Polytechnique Montreal, 2500 Chemin de Polytechnique, Montreal, QC H3T 1J4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2152-8045","authenticated-orcid":false,"given":"Mohamed-Salah","family":"Ouali","sequence":"additional","affiliation":[{"name":"Mathematics and Industrial Engineering Department, Polytechnique Montreal, 2500 Chemin de Polytechnique, Montreal, QC H3T 1J4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10845-016-1228-8","article-title":"A review of diagnostic and prognostic capabilities and best practices for manufacturing","volume":"30","author":"Vogl","year":"2019","journal-title":"J. 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