A method for automatically extracting harmonic features and its application in fault diagnosis of rolling bearing

With regard to fault diagnosis of rolling bearing, the envelope demodulation method is usually used to analyze the original vibration signal of faulty bearing, then the fault location of the bearing is determined by examining the distributions of fault characteristic frequencies, harmonics and sideband on the envelope demodulation spectral. The fault characteristic frequency with its harmonics could not be extracted by this traditional method when the original vibration signal is contaminated heavily by background noise. Besides, this method needs high professional knowledge and will expose drawbacks such as complex work, low diagnostic efficiency and so on while dealing with a large number of faulty bearings in engineering application. To solve the above problems, this paper proposes an automatic harmonic feature extraction method. Firstly, a series of bandpass filters are obtained based on fast Kurtogram, and then the original signal is filtered by the series of bandpass filters. The series of filtered signals are subjected to envelope analysis and noise reduction processing. Finally, the denoised series of envelope results are processed by the proposed algorithm for extracting the number of harmonics, harmonic intensity, and harmonic intensity index, and the target feature frequency and its harmonic components hidden in the original signal is extracted automatically. A simulation case and an engineering case verify that the proposed method can not only automatically calculate the number of harmonics of the characteristic frequencies, but also calculate the corresponding harmonic intensity, providing more effective and efficient feature support for fault diagnosis of bearing.