Aiming at the fault diagnosis of hydraulic pumps, a new fusion method was proposed based on the analysis mode decomposition(AMD), general morphological fractal dimensions(GMFD) and kernel fuzzy C-means clustering(KFCMC).Based on the priori knowledge about fault feature frequencies, the AMD was applied to decompose multi-mode vibration fault signals of a hydraulic pump in the effective range of bisecting frequency, and the best bisecting frequency for realizing optimal decomposition was chosen according to the Euclidean distance.Then, the mode extracted by virtue of the optimal bisecting frequency, which was rich in fault feature informations, was used as data sources to extract the GMFD and adopt it as feature vectors.Finally, the KFCMC was used to diagnose hydraulic pump faults.In addition, the methods of original AMD, experience mode decomposition(EMD), ensemble experience mode decomposition(EEMD), local mode decomposition(LMD), variational mode decomposition(VMD) and fuzzy C-means clustering(FCMC) were also used to decompose the signals, and it is shown that the proposed method is better than the others.Through the simulation and experiment verification on the fault signals of the tested hydraulic pump, it is shown that the proposed method is available to diagnose different hydraulic pump faults with an enough accuracy.
Key words
hydraulic pump /
analysis mode decomposition /
general morphological fractal dimensions /
kernel fuzzy c-means clustering
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