Bearing fault diagnosis based on wavelet transform and fuzzy inference
Bearing fault diagnosis based on wavelet transform and fuzzy inference
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DOI：
10.1016/s0888-3270(03)00077-3
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发表时间：
2004-09-01
影响因子：
8.4
通讯作者：
Loparo, KA
中科院分区：
文献类型：
--
作者：
Lou, XS;Loparo, KA
提出了一种基于小波变换和神经模糊分类的球轴承局部缺陷诊断新方案。采用电机驱动实验系统采集了正常轴承、内圈故障轴承和滚珠故障轴承的振动信号。利用小波变换对加速度计信号进行处理，生成特征向量。训练了自适应神经模糊推理系统（ANFIS），并将其作为诊断分类器。为了比较起见，还研究了欧几里得矢量距离法和矢量相关系数法。结果表明，所建立的诊断方法能够可靠地分离出存在负荷变化的不同故障状态。(C) 2003 Elsevier Ltd.版权所有。
This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations. (C) 2003 Elsevier Ltd. All rights reserved.