摘要
针对滚动轴承故障诊断过程中其诊断精度易受到非平稳、非线性振动信号的影响,提出了基于离散小波变换的多尺度模糊熵和最小二乘支持向量机的滚动轴承故障诊断方法。首先,将原始信号用离散小波变换分解得到若干近似系数,并在其重构得到近似分量后,运用相关系数和相关距离原则选取最优近似分量。再利用多尺度模糊熵获得特征向量,并输入至最小二乘支持向量机内进行故障识别。最后,利用凯斯西储大学轴承信号进行算法仿真验证。结果表明该方法识别精度达97%,进一步证明了该方法的可行性。
In view of the effects of non-stationary and nonlinear vibration signals on the accuracy of rolling bearing fault diagnosis,a rolling bearing fault diagnosis method was proposed based on discrete wavelet transform(DWT)and multi scale fuzzy entropy(MFE)and least squares support vector machine(LSSVM).Firstly,the original signal was decomposed by discrete wavelet transform to obtain some approximate coefficients,and after reconstructing the approximate components the optimal approximate components were selected by using the principle of correlation coefficient and correlation distance.Then the feature vector was obtained by multi scale fuzzy entropy and input into least squares support vector machine for fault recognition.Finally,the algorithm was simulated by using the bearing signal of Case Western Reserve University.The results show that the recognition accuracy of the method is 97%,which further proves the feasibility of the method.
作者
王凯峰
赵小惠
卫艳芳
WANG Kaifeng;ZHAO Xiaohui;WEI Yanfang(School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048,China)
出处
《西安工程大学学报》
CAS
2021年第5期80-85,99,共7页
Journal of Xi’an Polytechnic University
基金
陕西省教育厅专项科研计划项目(18JK0324)
陕西省社会科学界联合会项目(20ZD195-95)。
关键词
滚动轴承
故障诊断
离散小波变换
多尺度模糊熵
最小二乘支持向量机
rolling bearing
fault diagnosis
discrete wavelet transform
multiscale fuzzy entropy
least squares support vector machine
