Detection of weak fault using sparse empirical wavelet transform for cyclic fault
Detection of weak fault using sparse empirical wavelet transform for cyclic fault
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DOI：
10.1007/s00170-018-2553-1
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发表时间：
2018-11-01
影响因子：
3.4
通讯作者：
Liang, Steven Y.
中科院分区：
文献类型：
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作者：
Lu, Yanfei;Xie, Rui;Liang, Steven Y.
滚动轴承剩余使用寿命的成功预测取决于早期故障检测的能力。故障诊断的一个关键步骤是使用正确的信号处理技术来提取故障信号。本文提出了一种新开发的诊断模型，使用基于稀疏的经验小波变换（EWT），以提高故障的信噪比。该方法首先对未处理的信号进行峰波分析，定位故障频带，滤除系统噪声。然后，使用EWT对预处理后的信号进行滤波。实施l（q）-正则化稀疏回归以获得缺陷信号在频域中的稀疏解。所提出的方法表现出显着的改善的信号噪声比，适用于检测的周期性故障，其中包括轴承和齿轮箱的故障特征的提取。
The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The l(q)-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.