New technique for online faults diagnosis based on faulty models design: Application to DAMADICS actuator

Abstract

The increased complexity of plants and the development of sophisticated control system have necessitated the parallel development of efficient online fault detection and isolation system. The detection and isolation of faults in industrial system has lately become of great significance. This paper proposes a new technique for online fault detection and diagnosis in dynamic system with multi inputs multi outputs. Numerous diagnosis schemes and architectures have been developed and applied to the benchmark DAMADICS. One of the key issues in designing a fault diagnosis system is the system modeling. Neural networks combined with other methods have been widely investigated for that purpose. The main contribution of this paper is to develop a new method for online fault detection and diagnosis schema with a bank of fault free and faulty reference models designed according to neural networks. Fault detection is obtained according to the comparison of measured signals with the behavior of fault free reference model. Then, calculation of Euclidean norms of the output error signals resulting from the faulty models leads to fault isolation. The effectiveness of this approach is illustrated with simulations on DAMADICS benchmark.