Soft sensors for industrial fault detection using multi-scale fusion temporal convolutional autoencoders

With the widespread deployment of intelligent sensors and advances in data storage, large volumes of process data are continuously collected, providing a foundation for developing soft sensors for multi-scale monitoring in complex industrial processes. This paper proposes an enhanced autoencoder-based temporal convolutional soft sensor model for industrial process monitoring, aiming to effectively capture multi-scale features and the dynamic evolution of process data. The proposed filter temporal convolutional network incorporates adaptive filter-response normalization, thereby enhancing multi-scale feature extraction and improving model generalization. Then, a multi-layer filter temporal convolutional autoencoder is developed to enable efficient multi-scale feature extraction and accurate process data reconstruction. Moreover, a multi-scale feature fusion module with a channel attention mechanism is designed to adaptively integrate temporal features and significantly enhance model robustness. Finally, a statistical metric based on reconstruction errors is established, and the Kullback-Leibler divergence is employed to determine control limits for fault detection. The superiority and effectiveness of the proposed method are validated through applications to the wastewater treatment process and the multiphase flow process.