Machine fault diagnosis through an effective exact wavelet analysis
Machine fault diagnosis through an effective exact wavelet analysis
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DOI：
10.1016/j.jsv.2003.09.031
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发表时间：
2004-11-05
影响因子：
4.7
通讯作者：
Tam, HY
中科院分区：
文献类型：
--
作者：
Tse, PW;Yang, WX;Tam, HY
连续小波变换（CWTs）被广泛认为是基于振动的机械故障诊断的有效工具，因为它既可以检测平稳信号，也可以检测瞬时信号。然而，由于重叠问题，cwt生成的结果中存在大量冗余信息。重叠的出现会使光谱特征变得模糊，使机器操作员很难解释结果。对结果的误解可能导致误报或检测异常信号失败。此外，由于传统的CWTs仅使用单个母小波来生成子小波，因此在合成系数中不可避免地会对原始信号产生失真。显然，这将严重影响异常信号检测的准确性。为了减少重叠的影响，提高故障检测的准确性，设计了一种新的小波变换，称为精确小波分析，用于基于振动的机械故障诊断。基于遗传算法的精确小波分析设计。在每个选定的时间帧，算法将产生一个自适应子小波来尽可能精确地匹配被检测的信号。精确小波分析的优化过程与其他自适应小波分析不同，它既考虑小波系数的优化，又考虑小波可容许条件的满足。仿真和实际实验结果表明，精确的小波分析不仅可以最大限度地减少重叠的不良影响，而且可以帮助操作员检测故障并区分故障原因。在精确的小波分析的帮助下，可以避免由于机器的致命故障而导致的生产和服务突然停止。(C) 2003 Elsevier Ltd.版权所有。
Continuous wavelet transforms (CWTs) are widely recognized as effective tools for vibration-based machine fault diagnosis, as CWTs can detect both stationary and transitory signals. However, due to the problem of overlapping, a large amount of redundant information exists in the results that are generated by CWTs. The appearance of overlapping can smear the spectral features and make the results very difficult to interpret for machine operators. Misinterpretation of results may lead to false alarms or failures to detect anomalous signals. Moreover, as conventional CWTs only use a single mother wavelet to generate daughter wavelets, the distortion of the original signal in the resultant coefficients is inevitable. Obviously, this will significantly affect the accuracy in anomalous signal detection. To minimize the effect of overlapping and to enhance the accuracy of fault detection, a novel wavelet transform, which is named as exact wavelet analysis, has been designed for use in vibration-based machine fault diagnosis. The design of exact wavelet analysis is based on genetic algorithms. At each selected time frame, the algorithms will generate an adaptive daughter wavelet to match the inspected signal as exactly as possible. The optimization process of exact wavelet analysis is different from other adaptive wavelets as it considers both the optimization of wavelet coefficients and the satisfaction of the admissibility conditions of wavelets. The results obtained from simulated and practical experiments prove that exact wavelet analysis not only minimizes the undesirable effect of overlapping, but also helps operators to detect faults and distinguish the causes of faults. With the help from exact wavelet analysis, sudden shutdowns of production and services due to the fatal breakdown of machines could be avoided. (C) 2003 Elsevier Ltd. All rights reserved.