A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6139 |
| _version_ | 1797549545442246656 |
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| author | Hongchao Cheng Yiqi Liu Daoping Huang Chong Xu Jing Wu |
| author_facet | Hongchao Cheng Yiqi Liu Daoping Huang Chong Xu Jing Wu |
| author_sort | Hongchao Cheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:16:08Z |
| format | Article |
| id | doaj.art-bcf9f5b8be904812a8c7ca4a0337ffd7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:16:08Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bcf9f5b8be904812a8c7ca4a0337ffd72023-11-20T18:55:00ZengMDPI AGSensors1424-82202020-10-012021613910.3390/s20216139A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process MonitoringHongchao Cheng0Yiqi Liu1Daoping Huang2Chong Xu3Jing Wu4School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaProcess 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.https://www.mdpi.com/1424-8220/20/21/6139process monitoringfault diagnosisnonlinear large-scalesparse Bayesiantransfer learningprobabilistic relevance vector machine |
| spellingShingle | Hongchao Cheng Yiqi Liu Daoping Huang Chong Xu Jing Wu A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring Sensors process monitoring fault diagnosis nonlinear large-scale sparse Bayesian transfer learning probabilistic relevance vector machine |
| title | A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring |
| title_full | A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring |
| title_fullStr | A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring |
| title_full_unstemmed | A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring |
| title_short | A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring |
| title_sort | novel ensemble adaptive sparse bayesian transfer learning machine for nonlinear large scale process monitoring |
| topic | process monitoring fault diagnosis nonlinear large-scale sparse Bayesian transfer learning probabilistic relevance vector machine |
| url | https://www.mdpi.com/1424-8220/20/21/6139 |
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