Active Estimation of Multiplicative Faults in Dynamical Systems

TL;DR

This paper presents a real-time method for estimating multiplicative faults in linear systems, combining residual generation, regression, and optimal input design to improve accuracy and robustness.

Contribution

It introduces a novel fault estimator with an optimal input design framework, providing performance guarantees and efficient algorithms for enhanced fault estimation accuracy.

Findings

The proposed estimator effectively detects and estimates faults in simulations.

Optimal input design significantly improves estimation accuracy.

The method maintains robustness in noisy environments.

Abstract

This paper addresses the problem of estimating multiplicative fault signals in linear time-invariant systems by processing its input and output variables, as well as designing an input signal to maximize the accuracy of such estimates. The proposed real-time fault estimator is based on a residual generator used for fault detection and a multiple-output regressor generator, which feed a moving-horizon linear regression that estimates the parameter changes. Asymptotic performance guarantees are provided in the presence of noise. Motivated by the performance bounds, an optimal input design problem is formulated, for which we provide efficient algorithms and optimality bounds. Numerical examples demonstrate the efficacy of our approach and the importance of the optimal input design for accurate fault estimation.