Title: Online sensor ageing detection using a modified adaptive filter

Authors: Aqeel Madhag; Guoming Zhu

Addresses: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48823, USA; Al-Furat Al-Awsat Technical University, Kufa, Najaf Governorate 00964, Iraq ' Department of Mechanical Engineering and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48823, USA

Abstract: This paper proposes a fault detection algorithm to detect online sensor performance degradation and failure due to sensor ageing, where the sensor faults due to ageing are characterised by slow variations of the sensor measurement noise covariance matrix. The proposed algorithm utilises the information about the quality of weighted innovation sequence to estimate the slowly-varying sensor noise covariance. The iterative manner of the proposed algorithm leads to significant reduction of the computational load, reduced sensitivity to initial conditions and improved estimation accuracy, making it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor noise covariance for multiple-input and multiple-output systems with noise covariance varying linearly, exponentially, or linearly with sinusoid fluctuation. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature.

Keywords: sensor ageing noise; covariance identification; Kalman filter; covariance matching; discrete time-varying system.

DOI: 10.1504/IJAAC.2020.105518

International Journal of Automation and Control, 2020 Vol.14 No.2, pp.187 - 212

Received: 02 Apr 2018
Accepted: 13 Jul 2018
Published online: 03 Mar 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article