Enhancing fault detection in wastewater treatment plants: a multi-scale principal component analysis approach with the Kantorovich distance

Abstract

Anomaly detection in wastewater treatment plants (WWTPs) is critical for ensuring their reliable operation and preventing system failures. This paper proposes an advanced monitoring scheme that integrates multiscale principal component analysis (PCA) with a Kantorovich distance (KD)-driven monitoring approach to enhance WWTP monitoring in noisy environments. The combination of wavelet-based multiscale filtering with PCA effectively denoises the data, while the KD-driven scheme offers a robust metric for detecting deviations from normal operating conditions. This approach does not require labeled data and employs the nonparametric Kantorovich distance (KD) test, providing a flexible and practical solution for anomaly detection. Validation using data from the COST benchmark simulation model (BSM1) demonstrates the effectiveness of the proposed methods. The study evaluates different sensor faults—bias, intermittent, and aging—at varying signal-to-noise ratio (SNR) levels and explores the impact of different wavelet bases and decomposition levels on denoising and detection performance. The results show that the proposed scheme outperforms traditional PCA and multiscale PCA-based techniques, offering improved anomaly detection capabilities in the presence of significant noise.