自适应窗口旋转优化短时傅里叶变换的变转速滚动轴承故障诊断  

Fault diagnosis of rolling bearings under variable speed conditions based on adaptive window rotation optimization short-time Fourier transform

作　　者：赵一楠 剡昌锋[1] 孟佳东[2] 王宗刚[3] 王慧滨 吴黎晓[1] ZHAO Yi-nan;YAN Chang-feng;MENG Jia-dong;WANG Zong-gang;WANG Hui-bin;WU Li-xiao(School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China;School of Mechanical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;College of Physics and Electromechanical Engineering,Hexi University,Zhangye 734000,China;Department of Medical Technology,Zhangzhou Health Vocational College,Zhangzhou 363000,China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050 [2]兰州交通大学机电工程学院,甘肃兰州730070 [3]河西学院物理与机电工程学院,甘肃张掖734000 [4]漳州卫生职业学院医学技术学院,福建漳州363000

出　　处：《振动工程学报》2024年第6期1064-1076,共13页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(52365011,51765034);甘肃省科学计划项目(21JR7RA305)。

摘　　要：针对短时傅里叶变换(STFT)中固定窗效应所导致的能量集中度不高的问题,提出了一种自适应窗口旋转优化短时傅里叶变换(AWROSTFT)的变转速滚动轴承故障诊断方法。通过变分模态分解(VMD)对原始振动信号进行降噪,并利用粒子群优化算法(PSO)解决了VMD参数选择困难的问题;利用切线思想对STFT中水平窗口自适应匹配一系列的旋转算子,使得窗口旋转方向接近甚至等于瞬时调频率,提高了时频表示的能量集中度;计算出谱峰检测法提取到的瞬时频率与转频的平均比值,将得到的结果与轴承的故障特征系数进行匹配,以此实现变转速工况下滚动轴承的故障诊断。仿真和实验的结果都表明,本文所提方法能够兼顾PSO-VMD和AWROST-FT的优势,通过切线思想自适应的旋转窗口使得信号与窗函数在全局上的夹角都为零,从而达到提高能量集中度和锐化时频脊线的目的,实现了变转速工况下滚动轴承的故障诊断。This paper proposes a fault diagnosis method for rolling bearings under variable speed conditions,based on the Adaptive Window Rotation Optimization Short-Time Fourier Transform(AWROSTFT).This method addresses the issue of low energy concentration caused by the fixed window effect in Short-Time Fourier Transform(STFT).Variational Mode Decomposition(VMD)is used to reduce the noise of the original vibration signal,and Particle Swarm Optimization(PSO)is employed to solve the complex problem of VMD parameter selection.A series of rotation operators are adaptively matched to the horizontal window in STFT using the tangent idea,aligning the rotation direction of the window with the instantaneous frequency modulation to im-prove the energy concentration of time-frequency representation.The instantaneous frequency,extracted by the spectral peak detec-tion method,is divided by the frequency transformation curve.The result is matched with the fault characteristic coefficient of the bearing to achieve fault diagnosis of the rolling bearing under variable speed conditions.The results of simulation and experimental signals show that the proposed method effectively combines the advantages of PSO-VMD and AWROSTFT.Through the adap-tive rotation window with the idea of tangency,the angle between the signal and the window function is globally reduced to zero,improving energy concentration,sharpening the time-frequency ridge line,and enabling fault diagnosis of rolling bearings under variable speed conditions.

关 键 词：故障诊断 时频分析 自适应窗口旋转优化短时傅里叶变换 变分模态分解 变转速 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33