Variable-Structure Multiple-Model Approach to Fault Detection, Identification, and Estimation

Abstract

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A scheme is proposed to detect, identify, and estimate failures, including abrupt total, partial, and multiple failures, in a dynamic system. The new approach, named IM<formula formulatype="inline"><tex>$^{3}$</tex></formula>L, is developed based on variable-structure multiple-model estimation, which allows to improve performance by online adaptation. It uses an interacting multiple model estimator for fault detection and identification but the maximum likelihood estimator for estimating the extent of failure. It provides an effective and integrated framework for fault detection, identification, and state estimation. For two aircraft examples, the proposed approach is evaluated and compared with hierarchical multiple-model approaches and a widely used single-model residual-based generalized likelihood ratio approach in terms of detection and estimation performance. The results show that the IM<formula formulatype="inline"><tex>$^{3}$</tex></formula>L provides not only fast detection and proper identification, but also good estimation of the failure extent as well as robust state estimation. </para>