A novel data-driven fault detection scheme based on Kantorovich Distance (KD) is proposed for monitoring sensor faults in wastewater treatment plant (WWTP). Since WWTP is highly dynamic in nature, the dynamic principal component analysis (DPCA) modeling framework is used to incorporate dynamics of the process. In this paper, the Kantorovich Distance metric is combined with dynamic principal component analysis modeling framework. The KD metric computes the difference between two data sets and uses the difference as a measure of fault. The KD metric is computed between the residuals of normally operating data and the abnormal data. The effectiveness of KD fault detection metric is compared with T2, Q and generalized likelihood ratio(GLR) based fault indicators to detect bias, intermittent and drift faults in WWTP benchmark. The simulation results indicates the superiority of KD metric over T2, Q and GLR based fault indicators.
|Number of pages
|Published - 2022
|7th International Conference on Advances in Control and Optimization of Dynamical Systems, ACODS 2022 - Silchar, Assam, India
Duration: 22-02-2022 → 25-02-2022
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering