Unsupervised transfer learning for fault diagnosis across similar chemical processes

Fault diagnosis plays a crucial role in chemical processes to prevent major accidents. Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities significantly. However, the success of deep learning diagnosis models primarily relies on access to the extensive labeled dataset of faults. Another limitation is the variation of ambient temperature and raw material components contributing to diverse operating conditions. These challenges lead to the current fault diagnosis techniques being unsuitable for universal application across different devices. In this paper, a novel unsupervised transfer learning method called multiple source domain adaptation network (MSDAN) is proposed for industrial fault diagnosis across similar chemical processes. Owing to the limited availability of fault data in industrial manufacturing, fault samples for model training are expanded via an efficient generative adversarial network variant. Categorical features of various source domains are precisely extracted through distinct channels by an integration model of Transformer and multiscale convolutional neural network. Multi-channel Domain adaptation based on polynomial kernel-induced maximum mean discrepancy aligns the joint distributions of multiple domains and facilitates subsequent classification tasks. The effectiveness and robustness of the proposed method are demonstrated through experiments conducted on the multimode Tennessee Eastman process and the real-world fluid catalytic cracking process across different units.