An Integrated Distributed Fault Diagnosis Framework for Large-Scale Industrial Processes Based on Spatio–Temporal Causal Analysis

The networked structure of sensors emerges in large-scale industrial processes. Causal graphs can reveal the underlying mechanisms. However, due to the constraints of material and information flows, industrial process data exhibit complex spatio–temporal characteristics. Traditional causal discovery results include redundant information and the spatio–temporal features are not sufficiently mined, affecting the accuracy of fault diagnosis. To address the above problems, an integrated distributed fault diagnosis framework is proposed. First, a new method combining mechanism knowledge and correlation is proposed to construct a spatio–temporal causal graph, which highlight spatio–temporal causal information. Second, an embedded time convolutional network-based autoencoder is designed to extract spatio–temporal features simultaneously. Then, the local-global fault detection scheme is performed. On this basis, a new anomaly status information matrix is designed by decoder and spatial features to achieve root cause recognition. Finally, the effectiveness of the proposed method is validated using actual data from the hot strip mill process, achieving a fault detection accuracy of 98.3%.