Crankshaft Bearings Fault Diagnosis Based on SVD and Bispectrum
MACE '12 Proceedings of the 2012 Third International Conference on Mechanic Automation and Control Engineering(2012)
摘要
Singular value decomposition (SVD) can realize denoising without relying on spectral characteristics. It is more useful for small scale denoising. Bispectrum can effectively inhibit the interference of non Gaussian noise, which makes the signal feature extraction convenient. The two methods are combined in this research. In the beginning, the vibration signals of engine crankshaft bearings go through SVD-based denoising, and then the high-order spectral theory is adopted to get the bispectrum of the signals after denoising. In the end, the frequency band of the fault crankshaft bearings signal is extracted by searching the whole 2-D frequency field, and favorable diagnosing result is obtained.
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关键词
SVD-based denoising,small scale denoising,2-D frequency field,engine crankshaft bearing,fault crankshaft bearings signal,frequency band,high-order spectral theory,signal feature extraction,spectral characteristic,vibration signal,Crankshaft Bearings Fault
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