Journal
SENSORS
Volume 20, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/s20216139
Keywords
process monitoring; fault diagnosis; nonlinear large-scale; sparse Bayesian; transfer learning; probabilistic relevance vector machine
Funding
- National Natural Science Foundation of China [61873096, 62073145]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515011057]
- Guangdong Technology International Cooperation Project Application [2020A0505100024]
- Fundamental Research Funds for the central Universities, SCUT [D2201200]
- Science and Technology Planned Project of Guizhou Province [[2020]1Y276]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available