Abstract
As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency. It is of practical engineering significance to establish a high-precision algorithm diagnostic model for motor bearings. At present, in data-driven motor bearing fault diagnosis methods, the method of manually adjusting hyperparameters is usually adopted in complex network structure models with many hyperparameters. To realize the automatic optimization selection of hyperparameters, in this paper, a motor bearing fault diagnosis algorithm based on a convolutional long short-term memory network of Bayesian optimization (BO-CLSTM) is proposed. The algorithm combines the Bayesian optimization algorithm (BO), a long short-term memory network (LSTM) and the convolutional layer of a convolutional neural network (CNN). It saves the considerable workload of manually adjusting the hyperparameters, has good noise resistance, and realizes the true end-to-end motor bearing fault diagnosis. The proposed method is trained based on the original vibration signal of the bearing, and the accuracy of the final model reaches 100%. In addition, compared with other advanced fault diagnosis methods based on deep learning, the performance of the proposed method is significantly improved.
Highlights
W ITH the rapid development of science and technology, modern industrial technology has made great progress
In [13], aiming at fault visualization and automatic feature extraction, this paper presented a new and intelligent bearing fault diagnostic method by combining symmetrized dot pattern (SDP) representation with a squeeze-and-excitation-enabled convolutional neural network (SE-CNN) model
The algorithm combines the convolutional layer of the CNN and long shortterm memory network (LSTM) and uses the Bayesian optimization method to optimize the hyperparameters of the model
Summary
W ITH the rapid development of science and technology, modern industrial technology has made great progress. Li Yanfeng et al proposed a novel approach to rolling bearing fault diagnosis using singular value decomposition (SVD) and multiple deep belief network (DBN) classifiers This method reconstructs the vibration signals of rolling bearings under different conditions in phase space and obtains the feature matrix. The algorithm combines the convolutional layer of the CNN and LSTM and uses the Bayesian optimization method to optimize the hyperparameters of the model It can save the workload and time of hyperparameter adjustment and truly realize endto-end intelligent fault diagnosis technology. Based on the original time-domain signal, the motor bearing fault diagnosis model is established directly to obtain the weak features between the data to improve the accuracy of motor bearing fault diagnosis. The model reduces the influence of noise in the original signal, and the experimental results show that the model has good robustness
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