State estimation for predictive maintenance using Kalman filter

Abstract Failure can be prevented in time by preventive maintenance (PM) so as to promote reliability only if failures can be early predicted. This article presents a failure prediction method for PM by state estimation using the Kalman filter on a DC motor. An exponential attenuator is placed at the output end of the motor model to simulate aging failures by monitoring one of the state variables, i.e. rotating speed of the motor. Failure times are generated by Monte Carlo simulation and predicted by the Kalman filter. One-step-ahead and two-step-ahead predictions are conducted. Resultant prediction errors are sufficiently small in both predictions.

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