基于CGA-SVR的电主轴磨损故障诊断方法研究

电主轴是数控机床的一个重要功能部件,其优劣直接影响着工件质量,对电主轴进行故障诊断可以提高可靠性、降低生产成本。 因此采用混沌遗传算法(CGA) 优化的支持向量机回归模型( SVR) 进行电主轴故障诊断。 此方法利用主成分分析(PCA)对电主轴磨损故障振动信号的时、频域特征向量进行降维,将降维后的特征向量输入到经过 CGA 参数优化的 SVR 模型中并进行训练和测试。 结果表明,使用该模型对电主轴进行故障诊断,其训练和测试的准确率分别达到了 99. 272% 和95. 249%,可以实现对电主轴磨损故障进行准确诊断。

Research on wear fault diagnosis of motorized spindle based on CGA-SVR

Motorized spindle is an important functional part of CNC machine tool, and its advantages and disadvantages directly affect thequality of parts. A support vector machine regression model ( SVR) optimized by chaos genetic algorithm (CGA) is used for spindlefault diagnosis. The principle of the method is to use principal component analysis ( PCA) to reduce the dimensionality of the timefrequency characteristic vector of the vibration signal of electric spindle wear fault, and input the dimensionality reduced characteristicvector into the SVR model optimized by CGA parameters for training and testing. The results show that the accuracy of training andtesting is 99. 272% and 95. 249% respectively, which can diagnose the wear fault of motorized spindle accurately.