Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAS
Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAS
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DOI：
10.1016/j.ymssp.2006.11.003
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发表时间：
2007-07-01
影响因子：
8.4
通讯作者：
Hu, Qiao
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;He, Zhengjia;Hu, Qiao
提出了一种基于经验模式分解（EMD）、改进的距离估计技术和多自适应神经模糊推理系统（ANFIS）相结合的故障诊断新方法。该方法包括三个阶段。首先，在特征提取之前，对振动信号进行滤波、解调和经验模态分解等预处理，以获取更多的故障特征信息。然后，提取六个特征集，包括原始信号和预处理信号的时域和频域统计特征。其次，提出了一种改进的距离评价方法，并利用该方法从六个原始特征集中分别选择出六个显著特征集。最后，六个显着特征集输入到多个ANFIS与遗传算法（GAs）相结合，以识别不同的异常情况。将该方法应用于滚动轴承的故障诊断，实验结果表明，多ANFIS组合能够可靠地识别不同的故障类别和严重程度，与基于ANFIS的单个分类器相比，具有更好的分类性能。实验结果表明，基于改进的距离评价技术的特征选择方法是有效的。(c)2006爱思唯尔有限公司保留所有权利。
This paper presents a novel method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs). The method consists of three stages. First, prior to feature extraction, some preprocessing techniques, like filtration, demodulation and EMD are performed on vibration signals to acquire more fault characteristic information. Then, six feature sets, including time- and frequency-domain statistical features of both the raw and preprocessed signals, are extracted. Second, an improved distance evaluation technique is proposed, and with it, six salient feature sets are selected from the six original feature sets, respectively. Finally, the six salient feature sets are input into the multiple ANFIS combination with genetic algorithms (GAs) to identify different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the multiple ANFIS combination can reliably recognise different fault categories and severities, which has a better classification performance compared to the individual classifiers based on ANFIS. Moreover, the effectiveness of the proposed feature selection method based on the improved distance evaluation technique is also demonstrated by the testing results. (c) 2006 Elsevier Ltd. All rights reserved.