Elastic Slow Feature Prototypical Network for Few-Shot Fault Diagnosis of Industrial Processes

Most industrial process data are highly dynamic and nonlinear with time-varying characteristics, which poses great challenges to industrial fault diagnosis tasks. In addition, in actual industries, limited training samples are often obtained due to safety requirements and high maintenance costs. Therefore, it is crucial to effectively extract representative features and identify faults in a scenario with limited data. To address these issues, this study proposes an elastic slow feature prototypical network (ESFPN) under the meta-learning framework for few-shot dynamic fault diagnosis. First, a Siamese embedding network is customized with a loss function for slow feature (SF) extraction, aiming at characterizing process states effectively. Second, an elastic mechanism is introduced to the metric-based prototypical network (PN), which employs elastic distances to emphasize the classification impact of more important prototype vectors. ESFPN utilizes the extracted nonlinear SFs as the elastic metric network inputs to construct SF prototypes, which significantly improves the fault classification performance. The performance of ESFPN and other advanced comparative methods is evaluated using the Tennessee Eastman process (TEP). The results demonstrate the significant superiority and effectiveness of the proposed method.