Transform principle of inner product for fault diagnosis
Transform principle of inner product for fault diagnosis
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发表时间：
2007
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通讯作者：
Wang Xiao-dong
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作者：
Wang Xiao-dong
傅立叶变换、短时傅立叶变换、小波变换和第二代小波变换被广泛应用于机械故障诊断。本文揭示了这些变换的本质是对具有各种基函数的信号进行内积变换，从动态信号中提取与基函数最相似的故障特征。故障特征提取采用了三角基、Gabor基、离散基、谐波基、拉普拉斯基、厄米特基、第二代小波基等多种基函数。汽轮发电机的损耗故障特征、齿轮箱的冲击摩擦征兆、高压汽轮机受蒸汽激励的故障特征等。采用合理的基函数或多基(多小波)对动态信号进行内积变换，可以得到有效的故障特征和正确的故障诊断。
Fourier transform,short time Fourier transform,wavelet transform and second generation wavelet transform are widely used for mechanical fault diagnosis.In this paper,it is revealed that the essence of these transforms is inner product transform for signals with various basis functions,from which fault feature being the most similar to basis function can be extracted from dynamic signals.A lot of basis functions such as trigonometric basis,Gabor basis,discrete basis,harmonic basis,Laplace basis,Hermitian basis,second generation wavelet basis,etc.have been adopted for fault feature extraction.Looseness fault feature of a turbo-generator,impulse friction symptom of gearbox,failure feature of high-pressure turbine excited by steam,and bearing defect of electric locomotive were extracted successfully.Provided that adopt reasonable basis functions or multi-bases(multiwavelet) for inner product transform of dynamic signals, effective fault features and correct fault diagnosis can be obtained.