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Use of autocorrelation of wavelet coefficients for fault diagnosis

机译：小波系数自相关在故障诊断中的应用

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摘要

This paper presents a novel time-frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.
机译：本文提出了一种基于连续时间小波系数（CWC）自相关的基于时频特征的齿轮故障诊断系统。此外，它介绍了齿轮箱振动信号的原始数学近似值，该近似值近似于齿轮箱产生的噪声振动信号的正弦分量，包括使用CWC自相关的初期齿轮故障和严重齿轮故障。首先，使用自相关函数消除了连续小波变换（CWT）的缺点。其次，介绍了CWC的自相关作为机器状态监测中故障识别的原始模式。第三，使用由八项组成的正弦求和函数来近似估计由普通变速箱（NGs）的CWC自相关所产生的周期性波形，以及发生初期和严重的齿轮故障（例如，轻微磨损，中等磨损和损坏） -齿轮）。换句话说，如本文报道的那样，通过生成的CWC自相关波形的正弦近似，可以减小振动信号的大小，同时显着降低频率分量的损失。

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