A Novel Quality-Related Distributed Fault Diagnosis Framework for Large-Scale Sequential Manufacturing Processes

Large-scale manufacturing processes are usually made up of multiple interrelated and distributed continuously subprocesses, which are transmitted and connected by information and quality flow. The characteristics of long processes, quality heritability between subprocesses, and dynamic-coupled variables bring severe challenges to conventional quality-related fault diagnosis. Against this background, a novel distributed diagnosis framework for quality-related faults is proposed in this article. First, the sequential manufacturing process is decomposed into multiple subprocesses based on mechanism knowledge. Second, a novel dual-attention quality-driven autoencoder method is designed as the model for local fault diagnosis. Deep nonlinear features are extracted under quality supervision; meanwhile, the dynamic information and the different correlations among variables are also considered. Then, based on the tandem structure of the manufacturing process, multiple dual-attention quality-driven autoencoder models corresponding to each subprocess are constructed and stacked into a distributed model. Bayesian inference is used to build global monitoring statistics. Moreover, after faults occur, the intervariable attention weights are achieved to identify faulty variables. Finally, the effectiveness and advantages of the proposed framework are demonstrated via a practical large-scale sequential manufacturing process, the hot strip mill process.