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On the application of a subspace-based fault detection method

In this paper, we address the problem of detecting faults modeled as changes in the eigenstructure of the associated linear dynamical system and its application to some industrial problems, such as mechanical structures The outlined method is based on both recent stochastic subspace-based identificatio methods and the statistical local approach to the design of detection algorithms. The key idea is to replace regular systematic inspection by condition-based inspection, i.e. inspection decided upon the continuous monitoring of the considered system based on sensor data The statistical local approach deals with the early detection [19] of slight deviations in usual working conditions, based on long samples of multiple sensors outputs measurements, it detects very small changes in the modal parameters [4,5] After a brief review of the theory, we will focus on the demonstration of the capability and efficiency of this approach using output-only samples measured on a few industrially relevant examples, such as a steel subframe structure. For each of these examples, we present the application of the method in order to detect slight changes in their eigenstructures.