Trend Feature-Based Anomaly Monitoring of Infrequently Measured KPIs in Wastewater Treatment Process

Anomaly monitoring of key performance indicators (KPIs) is the core to guarantee the stable operation of wastewater treatment process (WWTP). One issue that has not been considered in WWTP is that KPIs can only be sporadically sampled, which is not conductive to the real-time monitoring. To solve this problem, a trend feature-based anomaly monitoring method is proposed. First, a fused multistep prediction strategy is designed to establish the nonlinear relationship between infrequent KPIs and the variables, with adaptive algorithm updating the parameters. Second, an autoregressive model is used to represent the variation trends, and a convex optimization problem, with the balance of small residuals and stable trends, is solved to extract trend features of KPIs. Third, the monitoring index, based on the $\ell _{2}$-norm of the trend features, is utilized to identify the abnormal KPIs. The operating data from WWTP are applied to demonstrate the effectiveness of the proposed monitoring method.