Disentangled Feature Representation Based on Multiscale Content Learning in Industrial Heterogeneous Nonstationary Fault Detection

Fault detection is crucial for ensuring the reliability and safety of industrial processes, as it helps prevent equipment failures and minimizes downtime. It has wide applications in industrial processes and machine intelligence but remains unsolved problem. Major challenges include the variations in data distribution and temporal dynamics due to the nonstationary nature, which is driven by factors such as frequent switching between operational modes, aging of distributed control sensors, and environmental variability. Traditional fault detectors are suboptimal, while nonstationary-aided methods easily make biased detections toward the multiple complex nonstationary components. In this article, we present a new fault detection model for nonstationary processes through multiscale content learning to disentangle feature representations (MCD). MCD consists of three submodules: diverse content module (DCM), stationary and nonstationary disentanglement module (SNDM), and component reweighting and reconstruction module (CR2M). DCM enriches multiple representations via multiscale modeling of network informational content. SNDM disentangles the stationary and nonstationary (S-N) features from multiple local representations, which is formulated as an encoding-decoding-encoding process with a skip disentanglement and min-max constraint. CR2M further fuses local S-N features with an adaptive learnable reweighting strategy, achieving a fine-grained reallocation of the importance of global S-N attributes. Experimental results on a numerical process and a real-world process demonstrate the superiority of MCD in fault detection for nonstationary processes.