In the manufacturing industry, the demand for fault-prediction solutions is increasing to prevent unexpected downtimes and reduce maintenance costs. Although deep-learning methods have demonstrated excellent performance in this domain, the current methods typically overlook the analysis of variable and random processes within mixed-model production, which is a manufacturing strategy that offers flexibility and efficiency in satisfying diverse consumer demands. Hence, we propose the multiscale process-aware retention network (MPRNet), which segments a time series into multiscale patches, thus enabling the model to focus on local information within each production process and correlations across all production processes. Furthermore, the network incorporates a cross-channel interaction module designed to dynamically capture the interactions between various sensor data types using a graph attention network, as well as transmit fault information across processes using state equations. We validate our proposed model on the BBA stud welding gun dataset and four additional open case studies. Compared with other established fault-prediction and time-series models, the MPRNet demonstrates improved F1-score by 13.1% in the BBA case and consistently achieves the best or near-best results in the open case studies.
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