基于相关系数和BP神经网络的轴承剩余寿命预测

为了评估机械设备的退化状态,准确掌握轴承剩余寿命信息,提出了一种基于相关系数和BP神经网络(BP Neural Network,BPNN)模型的轴承剩余寿命预测方法。该方法包括线上和线下两个步骤,首先利用相关系数法对预测模型的输入特征进行约简,其次线下步骤是通过机器学习来构建轴承的退化模型,而线上步骤则是利用退化模型来预测轴承剩余寿命。通过对轴承的全寿命退化实验数据进行分析预测,该方法与传统的技术相比能降低预测误差,表明该方法能够有效地模拟轴承退化过程并预测轴承剩余有效寿命(RUL)。

Remaining Life Prediction of Bearing Based on Correlation Coefficient and BP Neural Network

In order to evaluate the degradation state of the mechanical equipment and master the information of the remaining life of the bearing accurately, a method for predicting the remaining life of bearings based on correlation coefficient and BP neural network(BPNN)model is presented. The proposed method includes two steps of online and offline, first use the correlation coefficient to reduce features of the prediction model, then the offline step is used to build a degradation model of the bearing by machine learning and the online step predicts the remaining life by using the degeneration model. By analyzing the experimental data of bearing full lifetime degradation, the method can reduce the prediction error compared with traditional technology, the results show that the method can effectively simulate the bearing degradation process and predict the remaining useful life(RUL) of the bearing.