Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS)

均方根 小波 振动 均方误差 快速傅里叶变换 滚珠轴承 熵(时间箭头) 数学 球(数学) 控制理论(社会学) 计算机科学 算法 声学 工程类 统计 数学分析 物理 人工智能 电气工程 机械工程 润滑 量子力学 控制(管理)
作者
Omid Rahmani Seryasat,Mahdi Aliyari Shoorehdeli,Farhang Honarvar,Abolfazl Rahmani
标识
DOI:10.1109/icsmc.2010.5642389
摘要

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy mean and root mean square is proposed. The analysis results from energy entropy and root mean square of different vibration signals show that the energy and root mean square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy mean and root mean square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and root mean square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.
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