Multi-objective complexity reduction for set-based fault diagnosis

Abstract

Fault diagnosis methods ensure safe operation of industrial plants. Steadily increasing appearance of larger and interconnected systems and the necessity to take process uncertainties into account drives the need for reliable diagnosis procedures. Set-based frameworks for model-based fault diagnosis allow to handle these challenges, albeit at a high cost of computations. We propose a method to reduce the complexity of polynomial discrete-time models that retain the guarantee of fault detection. The relaxation-based method substitutes uncertain parts of model dynamics which are not relevant to diagnosing the fault. The method is illustrated with a fault detection example for an automatic air conditioning system of a building.