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Wavelet Transform in Vibroacoustic Diagnostics of Combustion Engines

机译:小波变换在燃烧发动机的Vibro声学诊断中

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This article discusses the use of wavelet decomposition in the diagnostics of vibrometric signals of an engine. Apart from presenting the possibility of using wavelets in diagnostics, the authors take up the subject of the applicability range of processing for stationary signals, which until now has been reserved for non-stationary signals. A unified definition of signal stationarity has been proposed, which is not based on statistics. The authors presented methods of wavelet decomposition of a vibrometric signal of combustion engine vibrations, measured with the use of LDV (Laser Doppler Vibrometry). Laser measurements allow for studying an object without "touching" its housing. Basing on the relative velocity of engine vibrations, the authors indicate how reliable vibrations are in diagnostics. Despite higher costs, this measurement method gives better results (for specific cases) than acoustic studies. Transform-wavelet decomposition is a solution hardly ever used in machine diagnostics; it is more often applied in medicine and image recognition. The authors presented the differences that can be obtained for different levels of decomposition, and also presented the impact on the engine condition assessment through the use of filtering (windowing) the signal before decomposition.
机译:本文讨论了小波分解在发动机振动信号的诊断中的使用。除了在诊断中使用小波的可能性外,作者将占据静止信号处理的适用范围的主题,直到现在已经保留了非静止信号。已经提出了一种统一的信号定义,这不是基于统计数据。作者提出了使用LDV(激光多普勒振动器)测量的燃烧发动机振动的振动信号的小波分解方法。激光测量允许在没有“触摸”其外壳的情况下进行物体。基于发动机振动的相对速度,作者表示如何可靠的振动在诊断中。尽管成本较高,但这种测量方法提供了比声学研究更好的结果(针对具体情况)。变换小波分解是一种难以用于机器诊断的解决方案;更常用于医学和图像识别。作者提出了可以获得不同水平的分解可以获得的差异,并且还通过在分解之前使用过滤(窗口)信号来提出对发动机条件评估的影响。

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