Improved Ensemble Superwavelet Transform for Vibration-Based Machinery Fault Diagnosis
Improved Ensemble Superwavelet Transform for Vibration-Based Machinery Fault Diagnosis
复制标题
改进的集成超小波变换用于基于振动的机械故障诊断
DOI：
10.1115/1.4032568
复制
发表时间：
2016-07
影响因子：
4
通讯作者：
Chen, Binqiang
中科院分区：
文献类型：
--
作者：
He, Wangpeng;Zi, Yanyang;Wan, Zhiguo;Chen, Binqiang
在前人的工作中，作者提出了一种基于可调Q因子小波变换（TQWT）和Hilbert变换相结合的集成超小波变换（ESW）的故障特征自动提取方法。然而，非平稳故障特征比的定义，以指导最佳小波基的选择，没有考虑高频分量的干扰。此外，原始ESW利用一个最佳子带来重构信号，这可能导致有用的故障特征的泄漏。本文针对这些问题对ESW进行了改进。具体地说，作者修改了故障特征比的定义，在计算希尔伯特包络谱的总幅值时消除了高频分量。此外，为了更好地保留故障特征，更准确地恢复信号，本文提出了一种新的融合两个最优子带的故障重构方法。通过对两个仿真信号的处理，验证了改进后的ESW的有效性和实用性。此外，改进的ESW被应用于一系列的工程应用，所获得的结果表明，改进的ESW可以作为一种有效的技术，在提取弱故障特征。
In the previous work of authors, the authors have presented an automatic fault feature extraction method, called ensemble superwavelet transform (ESW), based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform. However, the nonstationary fault feature ratio which defined to guide the optimal wavelet basis selection does not take the interferences of high-frequency components into consideration. In addition, the original ESW utilizes one optimal subband to reconstruct the signal, which may result in the leakage of useful fault features. The present paper improves the ESW to address these problems. Specifically, the authors modify the definition of fault feature ratio by eliminating the high-frequency components when calculating total amplitudes of Hilbert envelope spectrum. Moreover, for the purpose of preserving more useful fault features and recovering the signal more accurately, a novel approach to reconstruct the processed result by incorporating two optimal subbands is proposed in this paper. The comprehensive comparisons by processing two simulation signals are provided to verify the effectiveness and utility of the improved ESW. Moreover, the improved ESW is applied to a range of engineering applications, and the obtained results demonstrate that the improved ESW can act as an effective technique in extracting weak fault features.
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影响因子：
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作者：
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发表时间：
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期刊：
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影响因子：
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影响因子：
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