滚动轴承自适应组合状态预测方法研究

滚动轴承的状态预测组合模型中配比权重多为固定权重,自适应动态调整权重的组合型状态预测方法较少。为解决此问题,提出一种基于ARIMA与Elman的轴承自适应组合状态预测方法;采用IMS提供的轴承加速性能退化数据集进行验证。结果表明:使用单一ARIMA模型的预测相对误差为3.95%,使用单一Elman模型的预测相对误差为5.62%,而使用文中提出的变权重Elman-ARIMA组合预测模型的平均相对误差为3.22%,低于2种单一预测模型,预测结果具有更高的可靠性,证明了组合预测方法的可行性。

Research on the Prediction Method of the Adaptive Combined State of Rolling Bearing

In the state prediction combination model of rolling bearings, the proportion weights are mostly fixed weights, and there are few combined state prediction methods that adaptively dynamically adjust the weights. To solve this problem, an adaptive combined bearing state prediction method based on ARIMA and Elman was proposed; the bearing acceleration performance degradation data set provided by IMS was used for verification. The results show that by using a single ARIMA model and Elman model for prediction, the average relative errors are 3.95% and 5.62% respectively, and the average relative error of variable weight Elman-ARIMA combined prediction model is 3.22%, which is lower than the two single prediction models, and the prediction results have higher reliability, which proves the feasibility of the combined prediction method.