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Automatic gear and bearing fault localization using vibration and acoustic signals

机译:利用振动和声音信号自动定位齿轮和轴承故障

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The vibration and the acoustic responses from a rotary system in a given situation are not identical. The commencement of signal bursts in presence of defect has been investigated to design a robust system of filters, which can perform adequately for both the class of signals captured from geared and bearing systems for a wide range of faults. The proposed system of filters has several stages of signal processing such as denoising, time-frequency analysis, extraction of smooth envelope signal (SES) followed by a robust peak detection technique. First, the strength of wavelet packet transform (WPT) has been exploited along with a proposed algorithm to identify the denoised signal for further processing. In the second stage, the SES has been generated by integrating the enhanced,spectrogram coefficients in time domain. The corresponding enhanced time-frequency spectrogram has been generated by adopting complex Monet wavelet transform (CMWT) followed by a thresholding routine. As the objective is to localize faults in time domain signals, in the last stage, a robust peak detection technique has been integrated in the proposed system of filters. In all the aforementioned stages of filter design a two stage validation process has been followed. This involves a performance analysis with a synthetic signal followed by an experimental investigation. The strength of a strong signal preconditioning, which helps in identifying an appropriate mother wavelet function for different systems and for a wide range of faults, has beep highlighted. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在给定情况下,来自旋转系统的振动和声学响应并不相同。已经对存在缺陷时信号突发的开始进行了研究,以设计一个健壮的滤波器系统,该滤波器系统对于从齿轮和轴承系统捕获的各类信号,对于各种故障均能充分发挥作用。所提出的滤波器系统具有信号处理的多个阶段,例如去噪,时频分析,平滑包络信号(SES)的提取以及可靠的峰值检测技术。首先,已经利用小波包变换(WPT)的强度以及提出的算法来识别去噪信号,以进行进一步处理。在第二阶段,通过在时域中集成增强的频谱图系数来生成SES。通过采用复数Monet小波变换(CMWT)和阈值处理例程,可以生成相应的增强型时频频谱图。由于目标是定位时域信号中的故障,因此在最后阶段,已将鲁棒的峰值检测技术集成到所提出的滤波器系统中。在所有上述过滤器设计阶段,都遵循了两个阶段的验证过程。这涉及使用合成信号进行性能分析,然后进行实验研究。蜂鸣声突出表明,强大的信号预处理功能有助于为不同的系统和各种故障确定合适的母小波函数。 (C)2015 Elsevier Ltd.保留所有权利。

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