Joint application of VMD and IWOA-PNN for Gearbox Fault Classification via Current Signal

In this paper, a new method for fault diagnosis of wind turbine gearbox based on intelligent learning of current signal is proposed, which is a fusion method based on variational modal decomposition(VMD) and IWOA-PNN classification. In view of the fundamental frequency component and noise interferen...

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Bibliographic Details
Published in:IEEE sensors journal 2023-06, Vol.23 (12), p.1-1
Main Authors: Luo, Jiesi, Chen, Yuguang, Huang, Qiaoyuan, Zhang, Shaohui, Zhang, Xinbo
Format: Article
Language:English
Subjects:
Algorithms
Classification algorithms
Current signal
Decomposition
Entropy
Fault diagnosis
Gearboxes
IWOA-PNN
Neural networks
Optimization
Optimization algorithms
Resonant frequencies
Self tests
Sensors
Signal classification
Smoothing methods
Vibrations
VMD
Wind turbines
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Summary:In this paper, a new method for fault diagnosis of wind turbine gearbox based on intelligent learning of current signal is proposed, which is a fusion method based on variational modal decomposition(VMD) and IWOA-PNN classification. In view of the fundamental frequency component and noise interference of current signal, VMD is used to decompose the current signal to get the fault-related intrinsic mode functions (IMFs).The energy entropy of IMFs and some time domain and frequency domain indexes are selected to form a feature data set as the input of the IWOA-PNN classifier. In order to improve the classification effect of probabilistic neural network(PNN), an improved whale optimization algorithm (IWOA) based on the opposite based learning strategy (OBL) and the crisscross optimization algorithm is designed to select the optimal smoothing factor, namely IWOA-PNN. The effectiveness and superiority of the proposed method has been demonstrated by both public data and self-test data.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3269594