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Proceedings Paper

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

Paper Details

Date Published: 2 May 2017
PDF: 14 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001E (2 May 2017); doi: 10.1117/12.2262570
Show Author Affiliations
S. Andrew Gadsden, Univ. of Guelph (Canada)
T. Kirubarajan, McMaster Univ. (Canada)


Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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