基于指数小波阈值与PSO-DP-LSSVM的发动机轴承故障诊断  

Engine Bearing Fault Diagnosis Based on Exponential Wavelet Threshold and PSO-DP-LSSVM

作　　者：张亚靓 纪俊卿 孟祥川 许同乐[1] ZHANG Yajing;JI Junqing;MENG Xiangchuan;XU Tongle(School of Mechanical Engineering,Shandong University of Technology,Zibo Shandong 255000,China)

机构地区：[1]山东理工大学机械工程学院,山东淄博255000

出　　处：《机床与液压》2020年第19期196-200,共5页Machine Tool & Hydraulics

基　　金：山东省自然科学基金项目(ZR2016EEM20);新旧动能转换仪器仪表工程研究生导师能力提升模式研究(SDYY18101)。

摘　　要：针对小波软、硬阈值函数存在恒定偏差和不连续性的缺点,以及最小二乘支持向量机核函数参数选择困难等问题,提出了一种基于指数小波阈值与PSO-DP-LSSVM的发动机轴承故障诊断方法。利用指数小波阈值函数对原信号进行分解并重组,提取降噪后各个分量的能量特征;采用自适应的DP算法丰富PSO算法的解空间,并采用动态的参数控制,使其更容易获得最优解;将能量特征输入参数已定的LSSVM中,对信息进行训练和预测。结果表明:该方法能快速有效地对故障轴承信号进行自适应的故障诊断及分类。Aimed at the disadvantages of constant deviation and discontinuity of wavelet soft and hard threshold function and the difficulty in parameter selection of least-squares support vector machine kernel function, an engine bearing fault diagnosis method was presented based on exponential wavelet threshold and PSO-DP-LSSVM.The original signal was decomposed and reconstructed by using the exponential wavelet threshold function, and the energy feature of each component after noise reduction was extracted. Adaptive DP algorithm was used to enrich the solution space of PSO algorithm, and dynamic parameter control was adopted to get the optimal solution more easily. The energy feature was input into the LSSVM with determined parameters, and the information was trained and predicted. The results show that the fault bearing signals can be rapidly and efficiently diagnosed and classified by using the method.

关 键 词：指数小波阈值 最小二乘支持向量机 局域均值分解 粒子群算法 故障诊断 

分 类 号：TH165[机械工程—机械制造及自动化]