44. Condition-Based Failure Prediction

Machine reliability is improved if failures are prevented. Preventive maintenance (PM) can be performed in order to promote reliability, but only if failures can be predicted early enough. Although PM can be approached in different ways, according to time or condition, whichever one of these approaches is adopted, the key issue is whether a failure can be detected early enough or even predicted. This chapter discusses a way to predict failure, for use with PM, in which the state of a DC motor is estimated using the Kalman filter. The prediction consists of a simulation on a computer and an experiment performed on the DC motor. In the simulation, an exponential attenuator is placed at the output end of the motor model in order to simulate aging failure. Failure is ascertained by monitoring a state variables, the rotation speed of the motor. Failure times were generated by Monte Carlo simulation and predicted by the Kalman filter. One-step-ahead and two-steps-ahead predictions are performed. The resulting prediction errors are sufficiently small in both predictions. In the experiment, the rotating speed of the motor was measured every 5 min for 80 days. The measurements were used to perform Kalman prediction and to verify the prediction accuracy. The resulting prediction errors were acceptable. Decreasing the increment time between measurements was found to increase the accuracy of Kalman prediction uses. Consequently, it is shown that failure can be prevented (promoting reliability) by performing predictive maintenance depending on the results of state estimation using the Kalman filter.