Fault Feature Extraction Method of Rolling Bearings Based on CITD and FastICA

To solve the problem that vibration signal is easily affected by noise, which leads to the difficulty of fault feature extraction. This paper proposes a method for rolling bearing fault feature extraction which based on Cubic spline interpolation Intrinsic Time-scale Decomposition (CITD) algorithm and Fast Independent Component Analysis (FastICA) algorithm. Firstly, CITD method was used to decompose the original fault vibration signal, and a series of Proper Rotation (PR) components were obtained. Then the components with abundant fault information are selected by kurtosis criterion to reconstruct the observed signal, and the remaining components are reconstructed to obtain the virtual noise channel signal. FastICA algorithm is used to reduce the noise of reconstructed signal. The Teager-Kaiser Energy Operator (TKEO) is used to demodulate the signal after noise reduction. Finally, FFT transform is applied to the demodulated signal, the fault feature information of the original signal is extracted. Through comparative experiments, the results show that the proposed method can extract the fundamental frequency and frequency doubling characteristic information of rolling bearing faults more clearly and effectively.