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.