Open AccessRecognition of rolling bearing running state based on genetic algorithm and convolutional neural network
Author(s) -
Wanjie Lu,
Hongpeng Mao,
Fanhao Lin,
Zilin Chen,
Hua Fu,
Ya Xu
Publication year - 2022
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/16878132221095635
Subject(s) - hyperparameter , convolutional neural network , genetic algorithm , computer science , chromosome , fitness function , artificial intelligence , hyperparameter optimization , pattern recognition (psychology) , crossover , algorithm , machine learning , support vector machine , biochemistry , chemistry , gene
In this study, the GA-CNN model is proposed to realize the automatic recognition of rolling bearing running state. Firstly, to avoid the over-fitting and gradient dispersion in the training process of the CNN model, the BN layer and Dropout technology are introduced into the LeNet-5 model. Secondly, to obtain the automatic selection of hyperparameters in CNN model, a method of hyperparameter selection combined with genetic algorithm (GA) is proposed. In the proposed method, each hyperparameter is encoded as a chromosome, and each hyperparameter has a mapping relationship with the corresponding gene position on the chromosome. After the process of chromosome selection, crossover and variation, the fitness value is calculated to present the superiority of the current chromosome. The chromosomes with high fitness values are more likely to be selected in the next genetic iteration, that is, the optimal hyperparameters of the CNN model are obtained. Then, vibration signals from CWRU are used for the time-frequency analysis, and the obtained time-frequency image set is used to train and test the proposed GA-CNN model, and the accuracy of the proposed model can reach 99.85% on average, and the training speed is four times faster than the model LeNet-5. Finally, the result of the experiment on the laboratory test platform The experimental results confirm the superiority of the method and the transplantability of the optimization model.