Reliability Prediction Method of a Rolling Bearing Based on Mathematical Morphology and IFOA-SVR

In order to ensure the accuracy of the reliability prediction of a rolling bearing and increase the prediction step length, a rolling bearing reliability prediction method is proposed based on the fractal dimension of mathematical morphology and improved fruit fly optimization algorithm-support vector regression(IFOA-SVR). The envelope signal of the vibration signal is extracted and the fractal dimension of mathematical morphology of the envelope signal is calculated which is regarded as the performance degradation state feature of the rolling bearing. The IFOA is used to optimize the parameters C, g and ε of SVR simultaneously, the IFOA-SVR prediction model is established. At the same time, the Weibull proportional hazard model(WPHM) can be established using the maximum likelihood estimation combined with IFOA, then the reliability model can be obtained. The performance degradation state feature is regarded as the input of the IFOA-SVR prediction model, the long-term iterative prediction method is used to obtain the prediction results of the feature, and the results are embedded in the reliability model, then the reliability of the rolling bearing running state can be predicted. Experimental results show that the proposed method can be used for the reliability prediction of a rolling bearing, and the prediction step length can be increased on the premise that the prediction accuracy is high.