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Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter
Shock and Vibration ( IF 1.6 ) Pub Date : 2021-03-02 , DOI: 10.1155/2021/6690966
Quanfu Li 1, 2 , Yuxuan Zhou 3 , Gang Tang 3 , Chunlin Xin 3 , Tao Zhang 4
Affiliation  

The early weak fault characteristics of rolling bearings are extremely weak and are easily overwhelmed by other noises. In order to effectively extract the characteristics of the early weak faults of the rolling bearings and draw on the multilayer wavelet decomposition idea, a method for diagnosing the early weak faults of the rolling bearing based on the multilayer reconstruction filter is proposed. As we all know, empirical wavelet transform (EWT) makes full use of wavelet filter bank, and variational mode decomposition (VMD) uses Wiener filter bank. This paper fully combines the advantages of the above two methods, adaptively determines the number of modes through empirical wavelet decomposition and divides the original signal, extracts the frequency band that contains the fault characteristic information, and effectively eliminates noise interference. These steps are repeated until the optimal component of the condition is obtained. In the output layer, the weak fault impact components are further separated by the strong filtering and signal decomposition capability of VMD. The advantages of the proposed method are proved by the experiment of weak fault of rolling bearing and the accelerated failure experiment of full life. The proposed method has the advantages of reducing noise influence and adaptive estimation of decomposed modes, which can be applied more efficiently in practice.

中文翻译:

基于多层重构滤波器的滚动轴承早期薄弱故障诊断。

滚动轴承的早期弱故障特性非常弱,很容易被其他噪音淹没。为了有效地提取滚动轴承早期弱故障的特征并借鉴多层小波分解思想,提出了一种基于多层重构滤波器的滚动轴承早期弱故障的诊断方法。众所周知,经验小波变换(EWT)充分利用了小波滤波器组,而变分分解(VMD)使用了维纳滤波器组。本文充分结合了上述两种方法的优点,通过经验小波分解自适应地确定模式数,对原始信号进行分割,提取出包含故障特征信息的频带,并有效消除噪音干扰。重复这些步骤,直到获得条件的最佳分量为止。在输出层,VMD强大的滤波和信号分解能力将故障影响较小的组件进一步分离。通过滚动轴承的弱故障实验和全寿命加速失效实验,证明了该方法的优点。所提出的方法具有减少噪声影响和自适应估计分解模式的优点,可以在实践中更有效地应用。通过滚动轴承的弱故障实验和全寿命加速失效实验,证明了该方法的优点。所提出的方法具有减少噪声影响和自适应估计分解模式的优点,可以在实践中更有效地应用。通过滚动轴承的弱故障实验和全寿命加速失效实验,证明了该方法的优点。所提出的方法具有减少噪声影响和自适应估计分解模式的优点,可以在实践中更有效地应用。
更新日期:2021-03-02
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