Abstract
A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.
Highlights
The gearbox is an important part of the mechanical transmission
In order to highlight the characteristics of the signal, the kurtosis value is used as the characteristic parameter of the signal to extract, and a gear fault diagnosis based on the kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed
It can be seen from the results of the above gear fault signal after empirical mode decomposition (EMD) decomposition and from the frequency-domain diagram of the intrinsic mode functions (IMFs) that the phenomenon of mode mixing occurred in the frequency-domain, and the false component was decomposed, whose frequency value was almost close to 0, without any practical significance.From the results of VMD decomposition, it can be seen that this method can effectively avoid the phenomenon of mode mixing and endpoint effect in the EMD algorithm, and has certain help to improve the accuracy of gear fault diagnosis
Summary
The gearbox is an important part of the mechanical transmission. The failure of the gear in the gearbox will lead to the damage of the system, which will be paralyzed in serious cases. Based on the studies in the above literatures, due to the problems of mode mixing and endpoint effect in EMD and LMD methods, VMD was adopted for signal decomposition in this paper.At the same time, VMD is difficult to determine the penalty factor and the number of modal decomposition in the decomposition process, so the instantaneous frequency mean change of each component of the fault signal after VMD decomposition is put forward as an evaluation index, so as to determine the number of modal decomposition in the VMD process. In order to highlight the characteristics of the signal, the kurtosis value is used as the characteristic parameter of the signal to extract, and a gear fault diagnosis based on the kurtosis criterion VMD and SOM neural network is proposed
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