Particle and Kalman filtering for fault diagnosis in DC motors

Fault diagnosis is a major problem in industrial systems, and is of primary interest for mobile and industrial robotics where electric motors are used. In this paper fault diagnosis with the use of the Kalman filter is compared to fault diagnosis based on particle filter. The Kalman filter assumes linear model representation and Gaussian measurement noise whereas the particle filter is suitable for nonlinear models and does not make any assumption on the measurement noise distribution. The performance of the proposed methodology is tested through simulation experiments.