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Toward the use of wavelet scalograms in the diagnostic analysis of rotating machinery transient data

机译:在旋转机械瞬态数据的诊断分析中使用小波缩放标准

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Analysis of the vibration data has historically, and in practice today, been accomplished primarily using Fourier analysis which allows the diagnostician to review vibrational amplitudes and corresponding periodic frequencies generated by the machine. The most common application of Fourier analysis provides very limited information about the frequency content versus time, as these data are lost during the transform. This article presents a comparison analysis between Fourier transform and the wavelet transform, presented via the multilevel wavelet graphical presentation, or scalogram, on startup data from a large, vertical power plant pump driven by an electric motor. Wavelet scalograms also provide interesting and unexpected information such as frequency undulations during startup as well as intermittency of specific frequency components. As each method has its strengths and limitations, using the two methods together potentially provides a more complete picture of the vibration characteristics of the machine. Results of these analyses are presented with subsequent diagnostic analyses.
机译:振动数据的分析历史上以及今天在实践中,主要使用傅立叶分析来完成,这允许诊断师审查机器产生的振动幅度和相应的周期频率。傅立叶分析最常见的应用提供了关于频率内容与时间的非常有限的信息,因为这些数据在变换过程中丢失。本文介绍了通过多级小波图形呈现或标量程,从由电动机驱动的大型垂直发电厂泵的启动数据呈现的傅里叶变换和小波变换之间的比较分析。小波缩放图还提供了有趣和意外的信息,例如启动期间的频率波浪以及特定频率分量的间歇性。当每个方法都有其强度和局限性时,使用这两种方法一起潜在地提供了机器振动特性的更完整的图像。随后的诊断分析提出了这些分析的结果。

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