Modelling and identification for fault diagnosis: A new paradigm

A novel modelling, and identification scheme for real time fault diagnosis is proposed. A diagnostic model for the system is developed which consists of a) an input-output difference equation model expressed explicitly in terms of the feature vector (which is a vector formed of the coefficients of a transfer function model) and data vector (vector formed present and past inputs and the past outputs) and b) a matrix, termed multilinearity matrix, relating the diagnostic parameter (a vector formed of the coefficients of a transfer function of a functional unit) and the feature vector. A scheme to identify the diagnostic model in the face of model uncertainties, the noise and the disturbances is proposed.