Semi-Supervised Multitask Learning Approach Boosted by Operation Strategy Expert System for Industrial Process Fault Diagnosis

Fault diagnosis (FD) is crucial for keeping industrial processes safe. Although there is a wealth of historical running data stored, labeled fault data is insufficient. Semi-supervised learning mechanisms can fully utilize unlabeled data to enhance model generalization and performance. Expert systems (ESs) store rich expert knowledge and experience, providing additional decision information and experiential label support for neural networks (NNs). This study proposes a semi-supervised learning approach that combines ESs with NNs for industrial process FD. This approach adopts a multitask learning framework, wherein classification and meta-feature learning are the main and auxiliary tasks, respectively. A rule-based ES is constructed using a mixed integer linear programming (MILP) method. ESs make decisions based on meta-feature learning results, and during the backpropagation process, these decisions are used alongside predictions of the main task for parameter updates. The research findings suggest that the proposed approach has several advantages, including: 1) it enhances the model’s diagnostic performance, interpretability, and interactability; 2) it lessens the demand for labeled samples in the NN; and 3) it provides visual explanations for decision results. This study was validated on a simulated three-tank water storage platform and a gasification process case in a certain factory, achieving satisfactory results.