Dual-attention LSTM autoencoder for fault detection in industrial complex dynamic processes

Complex dynamic characteristics resulting from multi-system coupling and closed-loop control are ubiquitous in modern industrial process data, presenting significant challenges for process fault detection. However, conventional data-driven fault detection methods assume the data to be static or slightly dynamic. Addressing the complex dynamic characteristics and nonlinearity inherent in industrial processes, this paper proposes a dual-attention long short-term memory autoencoder (DALSTM-AE) for fault detection in dynamic processes. Long short-term memory (LSTM) and autoencoder (AE) are combined into a special encoder-decoder LSTM architecture to learn both dynamic features and deep representations of variables in an unsupervised manner. Then, a dual-attention module is embedded in the decoder to properly learn the temporal dependencies associated with long input sequences and retain the most critical information. In addition, based on the reconstruction results of the DALSTM-AE model, two monitoring statistics are designed for fault detection. Finally, the effectiveness and superiority of the proposed method are fully demonstrated through case studies on a numerical simulation example, the Tennessee Eastman (TE) benchmark process, and practical coal pulverizing systems in power plants.