Gear fault diagnosis based on energy entropy and adaptive neural fuzzy inference system

DOI:	10.11809/bqzbgcxb2024.04.038
Keywords:	gear; energy entropy; empirical mode decomposition; adaptive neuro fuzzy inference system; fault diagnosis
Abstract:	In order to solve the problem of severe noise pollution and difficulty in extracting fault feature information in gear vibration signals, a gear fault diagnosis method based on energy entropy and adaptive neural fuzzy inference system (ANFIS) is proposed. By decomposing the preprocessed vibration signal into adaptive noise complete empirical mode decomposition (CEEMDAN), eigenmode functions (IMFs) of different scales can be obtained; Due to the fact that each IMF contains main fault feature information and the distribution of each IMF is significantly different under different fault states, the time frequency domain features of faults are quantified by calculating energy entropy to construct feature vectors that represent modal component information; Using this input ANFIS for sample learning and training, the optimal ANFIS is obtained by adaptively adjusting network parameters and membership functions. The experimental results show that the diagnostic accuracy of this method is almost 100%, and it can effectively identify fault types.
Issue:	Vol. 45 No. 4 (2024)
Published:	2024-04-30
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