{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:32:05Z","timestamp":1768303925096,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T00:00:00Z","timestamp":1603843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873096"],"award-info":[{"award-number":["61873096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673181"],"award-info":[{"award-number":["61673181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020A1515011057"],"award-info":[{"award-number":["2020A1515011057"]}]},{"name":"Guangdong Technology International Cooperation Project Application","award":["2020A0505100024"],"award-info":[{"award-number":["2020A0505100024"]}]},{"name":"Science and Technology Planned Project of Guizhou Province","award":["[2020]1Y276"],"award-info":[{"award-number":["[2020]1Y276"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.<\/jats:p>","DOI":"10.3390\/s20216139","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T21:21:00Z","timestamp":1604006460000},"page":"6139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3102-1838","authenticated-orcid":false,"given":"Hongchao","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China"},{"name":"Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8911-727X","authenticated-orcid":false,"given":"Yiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China"}]},{"given":"Daoping","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China"}]},{"given":"Chong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China"}]},{"given":"Jing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China"},{"name":"School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, S., Cai, W., Liu, W., Shang, Z., and Liu, G. 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