Integrating Fault Diagnosis with Moving Horizon Estimation: A CSTR Case Study

Fault diagnosis and identification (FDI) is a critical aspect of process performance monitoring. In this work, statistical properties of decision variables of unconstrained Moving horizon estimation (MHE) are derived and further used for FDI. Once a fault is isolated, the fault magnitude refinement is carried out only for the isolated fault. Further, a hypothesis test is developed to terminate fault magnitude refinement when the fault magnitude saturates. When a sensor fault is isolated, the fault magnitude information is used for on-line compensation of measurements sent to the controller. The proposed approach is able to isolate and compensate for multiple single faults occurring sequentially in time and has embedded intelligence to carry out fault identification only when required. The efficacy of the proposed approach is demonstrated by simulating a non-isothermal CSTR system. Analysis of the simulation results underscore the effectiveness of the MHE-FDI scheme in correctly identifying faults in disturbance, actuator, and concentration measurements. Copyright (C) 2024 The Authors.