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The methodology aims to detect faults, identify their key causes and feature variables, and analyze the root path of fault propagation with the time and magnitude of one cause variable to another impact. This study proposed using a time domain multivariate granger-entropy-aided dynamic independent component analysis (DICA)\u2014distributed canonical correlation analysis approach, incorporating the dynamics time wrapping supported time delay-signed directed graph. The proposed methodology utilized the application to industrial and chemical processes and verified using the continuous stirred tank reactor and Tennessee Eastman process as practical application benchmarks. The framework\u2019s validations and efficiency are evaluated using established techniques such as classic computed ICA and DICA as standard model scenarios. The outcomes and results showed that the newly developed strategy is preferable to previous approaches regarding explainability and robust detection and identification of the actual root causes with high FDRs and low FARs.<\/jats:p>","DOI":"10.1088\/2632-2153\/ada088","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T22:59:30Z","timestamp":1734476370000},"page":"015005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4877-1045","authenticated-orcid":true,"given":"Husnain","family":"Ali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9889-4289","authenticated-orcid":true,"given":"Rizwan","family":"Safdar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-4726","authenticated-orcid":true,"given":"Yuanqiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0025-6175","authenticated-orcid":true,"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weilong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5900-1353","authenticated-orcid":true,"given":"Furong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"mlstada088bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2024.100709","article-title":"Advance industrial monitoring of physio-chemical processes using novel integrated machine learning approach","volume":"42","author":"Ali","year":"2024","journal-title":"J. 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