Research of multi-concurrent fault diagnosis of rotating machinery based on VMD and KICA

In order to improve the multi-concurrent fault diagnosis of rotating machinery, a feature extraction method based on variational mode decomposition (VMD) and kernel independent component analysis (KICA) is proposed. First, use VMD to improve the dimension of single-channel vibration signal. Then, calculate the correlation coefficient between the signal of each dimension and the original signal. Finally, high correlation signals are used to form a new observation signal and the fault signals will be extracted by KICA. Compared with ensemble empirical mode decomposition (EEMD) + fast independent component analysis (FastICA), the better performance of the proposed method is demonstrated by an analysis of rolling bearing with the fault of inner ring and outer ring mixed. Furthermore, an experiment with the fault of outer ring of rolling bearing and gear breaking mixed verifies the effectiveness of this method. The result demonstrates that the proposed method is efficient for fault diagnosis of single-channel vibration signal of rotating machinery with multi-concurrent faults