Abnormal Operating Condition Identification of Industrial Processes Based on Deep Learning With Global-Local Slow Feature Analysis

Ensuring long-term safe and efficient operation of industrial processes relies on real-time identification of abnormal operating conditions. However, industrial processes frequently switch among diverse operating conditions and face harsh production environments. As a result, some extreme cases exist in historical abnormal samples can mask some slight anomalies, making them show similar process dynamics to normal operating conditions. To address this issue, this study proposes a global-local slow-feature-analysis-based convolutional neural network (GLSFA-CNN). A global slow feature analysis (SFA) model extracts coarse-scale slow features at a macroscopic level to distinguish anomalies with different process dynamics, while a local SFA algorithm extracts real-time and fine-scale slow features at a microscopic level to identify anomalies with similar process dynamics. By combining global and local slow features, anomalies with similar or different dynamics can be simultaneously identified. The one-dimensional convolutional neural network (1-D-CNN) is then used to automatically extract deep features from the global-local slow features and identify abnormal operating conditions. The industrial experiment shows that the proposed method outperforms other traditional methods and achieves high anomaly identification accuracy for the industrial process with switching conditions and extreme cases.