A novel fault identification and root-causality analysis of incipient faults with applications to wastewater treatment processes

To detect incipient faults and identify the corresponding root-cause in the wastewater treatment processes, this paper proposed a novel fault diagnosis framework. In this framework, we firstly proposed a new method, called Moving average residual difference reconstruction contribution plot (Mard-RCP). Mard-RCP can solve the defect of traditional reconstruction-based contribution plot (RCP), which is not only required to obtain the sensor fault direction in advance, but also usually suffers from the fault smearing effect. Also, this method can effectively improve the diagnosis accuracy by manipulating the signal-to-noise ratio properly. Secondly, to address the traditional issue of contribution plot to identify root-variables regardless of causality, a novel fault candidate variables selection method, termed as VS-R, together with Granger causal (GC) analysis is introduced to locate the root-variables of the fault. The proposed fault diagnosis framework is simulated and validated on the BSM1 simulation platform proposed by International Water Association and in a full-scale wastewater treatment plant.