A robust fault detection and identification strategy for aerospace systems

Andrew S. Lee; Waleed Hilal; D. Ciampini; S. Andrew Gadsden; Mohammad Al-Shabi

doi:10.1117/12.2663917

14 June 2023 A robust fault detection and identification strategy for aerospace systems

Andrew S. Lee, Waleed Hilal, D. Ciampini, S. Andrew Gadsden, Mohammad Al-Shabi

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 1254707 (2023) https://doi.org/10.1117/12.2663917
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Fault detection and identification strategies utilize knowledge of the systems and measurements to accurately and quickly predict faults. These strategies are important to mitigate full system failures, and are particularly important for the safe and reliable operation of aerospace systems. In this paper, a relatively new estimation method called the sliding innovation filter (SIF) is combined with the interacting multiple model (IMM) method. The corresponding method, referred to as the SIF-IMM, is applied on a magnetorheological actuator which was built for experimentation. These types of actuators are similar to hydraulic-based ones, which are commonly found in aerospace systems. The method is shown to accurately identify faults in the system. The results are compared and discussed with other popular nonlinear estimation strategies including the extended and unscented Kalman filters.

Conference Presentation

Citation Download Citation

Andrew S. Lee, Waleed Hilal, D. Ciampini, S. Andrew Gadsden, and Mohammad Al-Shabi "A robust fault detection and identification strategy for aerospace systems", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 1254707 (14 June 2023); https://doi.org/10.1117/12.2663917

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