Non-linear minimum variance estimation for fault detection systems

Alkaya, Alkan and Grimble, Michael John (2015) Non-linear minimum variance estimation for fault detection systems. Transactions of the Institute of Measurement and Control, 37 (6). pp. 805-812. ISSN 0142-3312 (https://doi.org/10.1177/0142331214548304)

[thumbnail of Alkaya-Grimble-TIMC-2014-Non-linear-minimum-variance-estimation-for-fault]
Preview
 Text. Filename: Alkaya_Grimble_TIMC_2014_Non_linear_minimum_variance_estimation_for_fault.pdf
Accepted Author Manuscript
Download (310kB)| Preview

Abstract

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully.

CORE (COnnecting REpositories)