Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder

Fault detection consistently plays a crucial role in industrial dynamic processes as it enables timely prevention of production losses. However, since industrial dynamic processes become increasingly coupled and complex, they introduce uneven dynamics within the collected data, posing significant challenges in effectively extracting dynamic features. In addition, it is a tricky business to distinguish whether the fault that occurs is quality-related or not, resulting in unnecessary repairing and large losses. In order to deal with these issues, this paper comes up with a novel fault detection method based on quality-driven long short-term memory and autoencoder (QLSTM-AE). Specifically, an LSTM network is initially employed to extract dynamic features, while quality variables are simultaneously incorporated in parallel to capture quality-related features. Then, a fault detection strategy based on reconstruction error statistic squared prediction error ($SPE$) and the quality monitoring statistic Hotelling $T^2$ ($H^2$) is designed, which can distinguish various types of faults to realize accurate monitoring for dynamic processes. Finally, several experiments conducted on numerical simulations and the Tennessee Eastman (TE) benchmark process demonstrate the reliability and effectiveness of the proposed QLSTM-AE method, which indicates it has higher accuracy and can separate different faults efficiently compared to some state-of-the-art methods.