This paper addresses the problem of designing a fault identification and detection algorithm for non-linear systems. Timely identification and detection of a fault in a system is crucial in condition monitoring systems. However, finding the source of the failure is not trivial in systems
with large numbers of components and complex component relationships. In this paper, an efficient scheme to detect adverse changes in system reliability and find the failed component is proposed, based on the interacting multiple model (IMM) algorithm, with fault detection and diagnosis formulated
as a hybrid multiple model estimation scheme. The proposed approach provides an integrated framework for fault detection, diagnosis and state estimation. Its performance is illustrated for fault detection of a non-linear two-tank system. The proposed method can be used with different kinds
of filters, using the confusion matrix and classification accuracy as comparison metrics. A particle filter is used with the IMM algorithm and its performance is compared to the linear Kalman filter as a comparative case concerning the improvement that can be achieved when going beyond the
consideration that the system is linear.
Abrupt and incipient fault detection and compensation for a 4-tank system benchmark
