Fault Diagnosis of Engine Abnormal Sound Based on Wavelet Transform Technique
作者： Yuping ZANG
关键词: Engineering 、 Algorithm 、 Speech recognition 、 Spectral density estimation 、 Decorrelation 、 Wavelet packet decomposition 、 Autocorrelation technique 、 Wavelet 、 Second-generation wavelet transform 、 Harmonic wavelet transform 、 Stationary wavelet transform
摘要: The engine abnormal sound signal is proved to be non-stationary and carries intense background noise. In view of these characteristics, a denoising method using multilevel threshold based on the analysis autocorrelation detailed coefficients proposed. This use discrete wavelet transform technique decompose into approximations details. sequences are determined. According whether sequence reflects white noise, time-frequency map denoised then drawn through continuous transform. By combining features time domain frequency domain, faults can classified. experimental research simulated model introduced as an example. Piston cylinder knocking crank bearing knock. Which stand for familiar sounds compared analyzed. result proves that noise ratio increased higher- useful recovered. display corresponding fault which offers practical strategy diagnosis.
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