Abstract
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.
Highlights
Due to the increasing diversification of industrial demand, the combination of process and equipment results in system structures become increasingly complex
The source domain (SD) data are filtered according to labeled target domain (LTD) data, in such a way that SD data and LTD data have the same types of labels
BOD5, and Figure 6b shows the curve of the sludge index (SVI)
Summary
Hongchao Cheng 1,2 , Yiqi Liu 1, * , Daoping Huang 1 , Chong Xu 1 and Jing Wu 1,3. Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
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