School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Sensors (Basel). 2020 Oct 28;20(21):6139. doi: 10.3390/s20216139.
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.
过程监测在确保大型过程中设备的安全稳定运行方面起着重要作用。本文提出了一种新的基于数据驱动的过程监测框架，称为集成自适应稀疏贝叶斯迁移学习机（EAdspB-TLM），用于非线性故障诊断。所提出的框架具有以下优点：首先，重新推导了贝叶斯框架下的概率相关向量机（PrRVM），以便用于预测工厂的运行条件。其次，我们扩展了 PrRVM 方法，并将迁移学习纳入稀疏贝叶斯学习框架中，为其提供了迁移能力。第三，重新启用源域（SD）数据以缓解训练数据不足的问题。最后，将所提出的 EAdspB-TLM 框架有效地应用于监测实际的污水处理过程（WWTP）和田纳西州东曼化学过程（TECP）。结果进一步证明了该方法的可行性。
