Anomaly detection in pneumatic control valves using stacked re-optimized convolutional autoencoder and product quantization under imbalanced data conditions

Abstract Reliable operation of pneumatic control valves is essential for the safety and efficiency of industrial control systems. Existing data-driven anomaly detection methods are typically based on datasets containing faults from faulty valves. However, the scarcity of abnormal samples and tedious or time-consuming of data labeling in practice often lead to imbalanced datasets and unlabeled data, which pose challenges for the application of these methods. To address this problem, we propose a novel anomaly detection method for pneumatic control valves based on stacked re-optimized convolutional autoencoder (S-RCAE) and product quantization (PQ) technology. S-RCAE is designed as a feature extraction network to obtain the latent and essential feature representations of anomalies in the monitoring data. It is stacked by placing a sample selection layer with the DBSCAN clustering algorithm between the two CAEs. Furthermore, the two CAEs are modified by inserting a convolutional block attention module between every two layers. During the training of S-RCAE, the idea of few-shot learning and an improved cost-sensitive function are adopted for the labeling and feature extraction of anomaly samples in unlabeled and class-imbalanced datasets. Then, a feature fusion mechanism is implemented for the resulting representation from S-RCAE. The PQ is further introduced to enable dimensionality reduction and storage of the fused features and the accurate identification and assessment of anomalies using quantitative scores. The experimental results show that the proposed method can accurately identify and quantify the abnormal states of the valve from the unlabeled and class-imbalanced datasets.