振动
特征(语言学)
断层(地质)
信号(编程语言)
灵敏度(控制系统)
方位(导航)
蚁群优化算法
模糊逻辑
模式识别(心理学)
转速
旋转(数学)
状态监测
控制理论(社会学)
人工智能
计算机科学
工程类
声学
电子工程
物理
机械工程
哲学
语言学
电气工程
控制(管理)
地震学
程序设计语言
地质学
作者
Ké Li,Ping Xueliang,Huaqing Wang,Chen Peng,Yi Cao
出处
期刊:Sensors
[MDPI AG]
日期:2013-06-21
卷期号:13 (6): 8013-8041
被引量:66
摘要
A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.
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