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Due to the continuous technological innovation in industrial processes, many deep learning-based methods have shown powerful capability in handling equipment status monitoring, but most of them ignore the temporal features and the dynamic changes of the diverse spatial structure of the raw data. Meanwhile, these methods usually focus on handling a single downstream task but rarely consider different tasks simultaneously. To solve these issues, this article proposes a more flexible monitoring framework based on a dynamic-multilayer graph convolution network (GCN), which can be adapted to different downstream tasks simultaneously by agile combinations of the modules according to the different industrial scenarios. First, the time series of fault samples are segmented and constructed into a feature matrix to extract the temporal information by the temporal module. At the same time, to fully characterize the …