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首页> 外文期刊>Journal of Sound and Vibration >Adaptive fault feature extraction from wayside acoustic signals from train bearings
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Adaptive fault feature extraction from wayside acoustic signals from train bearings

机译：从火车轴承的自适应故障特征提取从路边声学信号

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摘要

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods. (C) 2018 Elsevier Ltd. All rights reserved.
机译：火车轴承故障的路边声学检测在维持铁路运输系统中的安全方面发挥着重要作用。然而，轴承故障信息通常由其他部件产生的强大的背景噪声和谐波干扰（例如轴和齿轮）掩盖。为了有效地提取轴承故障特征信息，本文提出了一种具有基于谐振的信号稀疏分解（RSSD）的改进的奇异值分解（ISVD）的新型方法，即ISVD-RSSD方法。 Savitzky-Golay（S-G）平滑过滤器用于在ISVD方法中滤除奇异矢量（SVS）作为奇异值分解（SVD）定理的延伸。希尔伯特谱熵和逐步优化策略用于优化S-G滤波器的参数。 RSSD方法能够非线性地分解出轴承的故障列车的路边声信号，其后者包含轴承故障信息。然而，高水平的噪声通常导致RSSD方法的差的分解结果。因此，必须首先使用ISVD-RSSD方法的ISVD分量首先去噪收集的路边声信号。接下来，通过使用RSSD方法分解去噪信号。然后通过Hilbert变换解调所获得的低共振分量，使得可以通过观察Hilbert封套光谱来检测轴承故障。根据本文所述的基于实验室场的实验，验证了ISVD-RSSD方法的有效性。结果表明，该方法优于传统的频谱分析和集合经验模式分解方法。 （c）2018年elestvier有限公司保留所有权利。

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