Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

S. Andrew Gadsden; T. Kirubarajan

doi:10.1117/12.2262570

2 May 2017 Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

S. Andrew Gadsden, T. Kirubarajan

Author Affiliations +

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 102001E (2017) https://doi.org/10.1117/12.2262570
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

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S. Andrew Gadsden and T. Kirubarajan "Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001E (2 May 2017); https://doi.org/10.1117/12.2262570

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