Using Singular value decomposition and high order spectrum for Bearings Fault Diagnosis

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.