Improving the Power of Fault Testing Using Reduced Order Models

In this paper we consider situations in which model based analytical redundancy is to be used to detect faults via a Neyman-Pearson approach to hypothesis testing. In [3] we examined how the statistical power of the resultant test statistic could be improved via the use of reduced order models. Here we extend the work to cover the effect of static non-linearities by using the stochastic embedding approach of [8], [7], [14], [16] and [5]. We complete the paper by showing how the proposed algorithm can be implemented recursively for on line applications and present some simulations examples to illustrate the superiority of the new algorithm over more conventional techniques. 1 Introduction The detection of changes in the dynamics of systems from noisy measurements has been well studied and is comprehensively documented in the survey chapters and books [19],[10], [1] and [2]. Two main approaches that have sprung from this work are Kalman Filter based approaches introduced by Mehra and Pe..