Feature-Augmented Broad Neural Network for Sensor Incipient Drift Fault Detection in Wastewater Treatment Process

Timely and accurate detection of sensor drift faults is critical for ensuring operational safety and compliance with effluent quality standards in the wastewater treatment process (WWTP). However, the slight and elusive nature of drift faults, coupled with the dynamic, nonlinear, and noisy characteristics of WWTP, poses a significant challenge to the detection of incipient drift faults. To address this problem, a novel feature-augmented broad neural network (FABNN) is proposed for sensor incipient drift fault detection. FABNN is constructed as a unified broad learning architecture, in which the feature layer, enhancement layer, and output layer are jointly redesigned to improve fault detection and faulty sensor localization accuracy. Specifically, a weighted feature filter (WFF) is devised for the feature layer to highlight low-frequency faults and suppress high-frequency noise. In the enhancement layer, a time-gated attention (TGA) mechanism is devised to capture the temporal characteristics of incipient drift faults, thereby enhancing fault feature extraction capability. In addition, an incremental learning version of FABNN is developed to dynamically extend the model structure without time-consuming retraining. To assess the performance of FABNN, it is evaluated on both benchmark and actual datasets from WWTP. The experimental results demonstrate that FABNN outperforms representative comparative methods in detecting incipient drift faults and locating faulty sensors at minimal sampling intervals after fault occurrence while achieving the highest accuracy and the lowest false and missed alarm rates (MARs). Moreover, FABNN shows strong robustness to various noise types and maintains stable detection performance for incipient drift faults.