KPCA和遗传BP神经网络在滚珠丝杠故障诊断中的应用研究

摘要

A fault diagnosis method of Ball screw based on KPCA and Genetic GA-BP neural networks was proposed. First,syn-chronous acquisition of vibration signal of the Ball screw using 6 sensors in 2 points was done,and the original sample space was ob-tained by feature extraction. Then the dimension of the original sample space was reduced with the KPCA to eliminate the redundant in-formation of the sample space. By introduced Genetic Algorithm,the randomness at selecting of traditional BP neural network initial weights and threshold was resolved,and three network in different types were established to diagnosis four different state of Ball screw including normal state,screw bending,broken ball and raceway wear. Results show that,ball screw fault diagnosis method based on KPCA and GA-BP neural network has significantly shorten the training time of the network,and effectively improve the recognition rate of the fault condition.%提出了一种基于核主元分析（KPCA）和遗传BP神经网络的滚珠丝杠故障诊断方法。首先用2个测点的6个传感器同步采集滚珠丝杠的振动信号，并进行特征提取，得到原始样本空间，然后利用核主元分析对原始样本空间进行降维处理，以消除样本间的冗余信息。引入遗传算法，解决了传统BP神经网络初始权值和阈值选择的随机性，并建立3种不同的滚珠丝杠故障诊断网络对滚珠丝杠的正常状态、丝杠弯曲、滚珠破损和滚道磨损4种状态进行诊断实验。结果表明：基于核主元分析和遗传BP神经网络的滚珠丝杠故障诊断方法明显地缩短了网络的训练时间，有效地提高了故障状态的识别率。