System representations in subspaces of finite-sample signals and their application to data-driven fault detection

TL;DR

This paper develops a data-driven fault detection method using system representations in finite-sample signal subspaces, leveraging singular value decomposition and matrix perturbation theory.

Contribution

It introduces a novel projection-based fault detection approach based on finite-sample image and residual subspaces, extending the fundamental lemma.

Findings

The method effectively detects faults using low-rank matrix approximations.

Analysis shows improved detection performance over existing methods.

The approach is grounded in a theoretical framework linking subspace representations and fault detection.

Abstract

This paper deals with system representations in finite-sample signal subspaces and their application to data-driven fault detection. The first part addresses concepts of finite-sample image and kernel system representations and, associated with them, image and residual subspaces of finite-sample signals. On this basis, the equivalence between the fundamental lemma and finite-sample image subspace is demonstrated. While the image representation models the nominal system dynamics, the residual representation describes uncertainties in the input-output data and is essential for fault detection. This result extends the fundamental lemma and builds the basis for exploring data-driven fault detection. In the second part, a data-driven projection-based fault detection approach is developed. By means of a singular value decomposition, orthogonal projections onto the image and residual subspaces are realized in the context of a low-rank matrix approximation, leading to projection-based residual generation and evaluation. Finally, analysis of detection performance in the framework of matrix perturbation theory and comparison with existing data-driven fault detection methods are explored.