PCA-Based Sensor Drift Fault Detection With Distribution Adaptation in Wastewater Treatment Process

Accurate detection of sensor drift fault in wastewater treatment process (WWTP) is essential for maintaining normal system operation and making correct decisions. However, since the WWTP is influenced by numerous internal and external factors, the data acquired from the actual WWTP is always multi-distributed, thus bringing difficulties to the accurate detection of sensor drift fault for the slow gradual change. To address this problem, a PCA-based sensor drift fault detection method with distribution adaptation (DAPCA) is proposed in this study. It presents a novel PCA-based fault detection method including a temporal WaveCluster for adaptive clustering for multi-distributed data, and a robust PCA-based fault detection with a smoothing mechanism using a combined index. Firstly, an improved WaveCluster algorithm is designed to cluster the multi-distributed data adaptively by considering both the spatial and temporal characteristics. Secondly, a robust PCA algorithm is presented that incorporates a smoothing mechanism to increase its robustness to noise interference. Thirdly, to strike a balance between traditional statistical indexes, a combined index is introduced with adaptive thresholds for multi-distributed data, thus enhancing the overall detection accuracy. To assess the performance of DAPCA, it is tested on both benchmark and real datasets. The results show that it attains the superior detection accuracy with higher F1-scores and lower false alarm rates than comparative methods. Furthermore, DAPCA is demonstrated to be more robust to various types of noises, significantly reducing the false alarms caused by the noise. Note to Practitioners—In the context of wastewater treatment process (WWTP), the inherent exposure of sensors to harsh environmental conditions renders them prone to drift fault. Furthermore, the complex operational dynamics of WWTP contribute to the emergence of a multi-distribution of data, thereby exacerbating the challenges associated with accurate detection of drift fault. Motivated by this, the present paper proposes a PCA-based sensor drift fault detection method with distribution adaptation (DAPCA) in WWTP, which prevents the degradation of detection accuracy caused by changes in data distribution. It presents a novel PCA-based fault detection method including a temporal WaveCluster for adaptive clustering for multi-distributed data, and a robust PCA-based fault detection with a smoothing mechanism using a combined index. Consequently, the effectiveness of the proposed DAPCA is validated via comparisons to other models, which performs a superior detection accuracy with higher F1-scores and lower false alarm rates. Furthermore, DAPCA is demonstrated to be more robust to many types of noises, significantly reducing the false alarms caused by the noise. In conclusion, for multi-distributed data, DAPCA is able to accurately detect sensor drift fault in WWTP, and can be further extended for sensor drift fault detection in other industrial processes.