Fault Detection, Isolation and Quantification from Gaussian Residuals with Application to Structural Damage Diagnosis

Michael Döhler 1 Laurent Mevel 1 Qinghua Zhang 1
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : Despite the general acknowledgment in the Fault Detection and Isolation (FDI) literature that FDI are typically accomplished in two steps, namely residual generation and residual evaluation, the second step is by far less studied than the first one. This paper investigates the residual evaluation method based on the local approach to change detection and on statistical tests. The local approach has the remarkable ability of transforming quite general residuals with unknown or non Gaussian probability distributions into a standard Gaussian framework, thanks to a central limit theorem. In this paper, the ability of the local approach for fault quan-tification will be exhibited, whereas previously it was only presented for fault detection and isolation. The numerical computation of statistical tests in the Gaussian framework will also be revisited to improve numerical efficiency. An example of vibration-based structural damage diagnosis will be presented to motivate the study and to illustrate the performance of the proposed method.
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Annual Reviews in Control, Elsevier, 2016, 42, pp.244-256. 〈10.1016/j.arcontrol.2016.08.002〉
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Michael Döhler, Laurent Mevel, Qinghua Zhang. Fault Detection, Isolation and Quantification from Gaussian Residuals with Application to Structural Damage Diagnosis. Annual Reviews in Control, Elsevier, 2016, 42, pp.244-256. 〈10.1016/j.arcontrol.2016.08.002〉. 〈hal-01376804〉

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