A Transformer-Based Fault Detection Approach and Its Application in Tandem Cold Rolling Process

Empowered by the abundant data in the Internet of Things, deep learning models, especially transformers, have achieved great success in the field of industrial process fault diagnosis. However, most existing transformer-based detection methods mainly rely on the temporal information of data while just implicitly considering the correlation between process variables. Moreover, the characteristic of ‘black box’ of transformer models often results in poor interpretability. To handle these problems, an interpretable dual-attention transformer (IDAformer) based fault detection method is proposed, which can explicitly capture both the temporal and variable dependencies. Specifically, an adaptive activation function and a gating mechanism are designed to allocate different activation strengths and weights for the time attention and variable attention, which can enhance network performance. Meanwhile, an interpretable attention mechanism and an attention visualization method are introduced to improve the interpretability. Moreover, state-space model is also integrated to guide the learning of network such that it can further enhance network performance. Finally, the effectiveness of the proposed method is verified through a tandem cold rolling process.