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Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive

机译:面向独立的VMD识别高速机车轮对轴承故障的特征

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摘要

As one of most critical component of high-speed locomotive, wheel set bearing fault identification has attracted an increasing attention in recent years. However, non-stationary vibration signal with modulation phenomenon and heavy background noise make it difficult to excavate the hidden weak fault feature. Variational Mode Decomposition (VMD), which can decompose the non-stationary signal into couple Intrinsic Mode Functions adaptively and non-recursively, brings a feasible tool. However, heavy background noise seriously affects setting of mode number, which may lead to information loss or over decomposition problem. In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing. To overcome the information loss problem, the appropriate mode number is determined by the criterion of approximate complete reconstruction. Then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem. Finally, three applications to wheel set bearing fault of high speed locomotive verify the effectiveness of the proposed method compared with original VMD, EMD and EEMD methods.
机译:作为高速机车最关键的部件之一,轮对轴承故障的识别近年来引起了越来越多的关注。然而,具有调制现象和较大背景噪声的非平稳振动信号使得难以挖掘隐藏的弱故障特征。可变模式分解(VMD)可以自适应地和非递归地将非平稳信号分解为耦合的固有模式函数,从而带来了一种可行的工具。但是,背景噪声过大会严重影响模式编号的设置,可能导​​致信息丢失或过度分解的问题。提出了一种通过相关性分析的面向独立VMD方法,自适应地提取轮对轴承的弱复合故障特征。为了克服信息丢失的问题,适当的模式号由近似完全重建的准则确定。然后根据其包络的相似度将相似模式进行组合,以解决过分解问题。最后,在高速机车轮对轴承故障的三个应用中,与原始的VMD,EMD和EEMD方法相比,该方法是有效的。

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