To improve the quality of the non-stationary vibration features and the performance of the variable-speed-condition fault diagnosis, this paper proposed a bearing fault diagnosis approach with Recurrence Plot (RP) coding and a MobileNet-v3 model. 3500 RP images with seven fault modes were obtained with angular domain resampling technology and RP coding and were input into the MobileNet-v3 model for bearing fault diagnosis. Additionally, we performed a bearing vibration experiment to verify the effectiveness of the proposed method. The results show that the RP image coding method with 99.99% test accuracy is superior to the other three image coding methods such as Gramian Angular Difference Fields, Gramian Angular Summation Fields, and Markov Transition Fields with 96.88%, 90.20%, and 72.51%, indicating that the RP image coding method is more suitable for characterizing variable-speed fault features. Compared with four diagnosis methods such as MobileNet-v3 (small), MobileNet-v3 (large), ResNet-18, and DenseNet121, and two state-of-the-art approaches such as Symmetrized Dot Pattern and Deep Convolutional Neural Networks, RP and Convolutional Neural Networks, it is found that the proposed RP+MobileNet-v3 model has the best performance in all aspects with diagnosis accuracy, parameter numbers, and Graphics Processing Unit usage, overcoming the over-fitting phenomenon and increasing the anti-noise performance. It is concluded that the proposed RP+MobileNet-v3 model has a higher diagnostic accuracy with fewer parameters and is a lighter model.
In the bearing fault diagnosis process using the convolution neural network (CNN), there are some problems, such as complex signal data processing and the complex network parameter setting. A rolling bearing fault diagnosis method is proposed to solve these problems based on improved particle swarm optimization and convolution neural networks with wide kernels in first-layer (IPSO-WCNN). The particle self-adaptive jump out algorithm is proposed to overcome particle swarm optimization (PSO) shortcomings. The adaptive inertia weight and the linear change acceleration coefficients are adopted for improved particle swarm optimization (IPSO). The convolution neural networks with wide kernels in first-layer (WCNN) fault diagnosis method is proposed for one-dimensional rolling bearing vibration signals, and the parameters of the WCNN is optimised by IPSO. According to the verification experiments, the proposed method can get higher accuracy than others with good adaptability.
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