Combination of probability approach and support vector machine towards machine health prognostics
This study presents a combined method of the probability approach and support vector machine (SVM) to predict failure degradation based on simulated and experimental failure bearing data. The failure rate as a degradation parameter is calculated using the Cox-proportional hazard model and the reliability theory based on simulated and experimental data. Kurtosis is used to show the bearing condition under specified operating conditions up to final failure occurrence. For simulated data, a failure degradation is calculated using the Cox model, where the baseline hazard is assumed having Weibull probability. In the case of experimental data, a reliability formula is employed to estimate the failure degradation of the bearing based on run-to-failure datasets. Both failure degradations are regarded as target vectors which indicate the bearing health to failure condition. Moreover, an SVM is employed as an artificial intelligence prognostics method and trained by kurtosis and the target vector to build the prediction model. The trained SVM is then utilized to predict the final failure time of individual bearing data. The result shows that the proposed method has the potential to be a machine health prognostics framework.