Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

Abstract

Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing. Due to the variations in material, equipment and environment, the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes. Despite much progress in statistical learning and deep learning for fault recognition, most models are constrained by abundant diagnostic expertise, inefficient multiscale feature extraction and unruly multimode condition. To overcome the above issues, a novel fault diagnosis model called adaptive multiscale convolutional neural network (AMCNN) is developed in this paper. A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data, embedding the adaptive attention module to adjust the selection of relevant fault pattern information. The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition. The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method. Compared with other common models, AMCNN shows its outstanding fault diagnosis performance and great generalization ability.