周期势增强随机共振机械故障特征提取方法  

Enhanced Stochastic Resonance Method with Periodic Potential and Its Application in Mechanical Fault Characteristic Extraction

作　　者：胡世军[1] 刘超 HU Shi-jun;LIU Chao(College of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;College of Mechanical and Electrical Engineering, Gansu Forestry Technological College, Tianshui 741020, China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050 [2]甘肃林业职业技术学院机电工程学院,甘肃天水741020

出　　处：《测控技术》2018年第12期89-93,共5页Measurement & Control Technology

摘　　要：针对机械设备早期故障特征难以提取的问题,提出一种周期势函数增强随机共振的机械故障特征提取方法。该方法利用周期势函数的无限稳态结构和抗粒子运动饱和特性,并整合频移尺度变换,能够克服经典双稳态随机共振方法的饱和问题,有利于高速高精机械设备旋转部件早期故障微弱特征的增强与提取。对仿真和电机轴承实验分别用提出方法、经典双稳态随机共振方法和集成经验模式分解方法进行故障特征提取,结果表明提出方法优于集成经验模式分解方法,而且比经典双稳态随机共振方法有更好的增强效果,能够增强和提取微弱故障特征,实现高速高精机械设备电机轴承的故障诊断。Since incipient fault characteristics in mechanical equipment are difficult to be extracted,an enhanced stochastic resonance(SR)method with periodic potentials is proposed.The proposed method can utilize the multistable structures of periodic potentials to overcome the saturated shortcoming of classical bistable SR.Moreover,the frequency-shifted and rescaled transform are fused into the proposed method for extracting large-parameter fault characteristics of high-speed and high-precision machine tool.Simulation and experimental data indicated that the proposed method was better than ensemble empirical mode decomposition in extracting weak fault characteristics and moreover there are higher amplitudes at fault characteristic frequencies as compared with classical bistable SR method,thereby the fault diagnosis of motor bearings of high-speed and high-precision machine tool can be achieved.

关 键 词：周期势 多稳态随机共振 故障特征提取 机械故障诊断 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]