A behavioral approach to direct data-driven fault detection

Efficient and reliable fault detection methods are needed for monitoring and evaluation of processes, e.g. , in structural health assessment. Existing methods rely on an a priori given model. Obtaining a model is nontrivial and is often the bottleneck in applications. Direct data-driven methods were recently developed in signal processing and control. These methods avoid parametric model identification and may outperform state-of-the-art model-based methods. This paper presents a direct data-driven method for fault detection. The monitored process is modeled as a linear time-invariant system with an unobserved deterministic disturbance. The behavioral approach to systems theory is used in order to define a representation invariant measure for the distance between data and model. The main contribution of the paper is a method for computing the distance directly from offline and online data without parametric model identification. Another contribution is a direct data-driven input estimation. The method is validated empirically on simulated data of a flexible beam with a crack, where the objective is to detect the crack.