scholarly journals An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1729 ◽  
Author(s):  
Shaobo Li ◽  
Guokai Liu ◽  
Xianghong Tang ◽  
Jianguang Lu ◽  
Jianjun Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Convolutional Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions

2019 ◽  
Vol 234 (4) ◽  
pp. 1167-1182 ◽  
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Jinrui Wang ◽  
Zenghui An ◽  
Yu Xin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Neural Network Model ◽  
Network Architecture ◽  
Deep Convolutional Neural Network ◽  
Mechanical Equipment

Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working conditions, such as noisy environment and different workloads. In this paper, a novel model named adaptive and fast convolutional neural network with wide receptive field is presented to overcome the aforementioned deficiencies. The prime innovations include the following. First, a deep convolutional neural network architecture is constructed using the scaled exponential linear unit activation function and global average pooling. The model has fewer training parameters and can converge rapidly and stably. Second, the model has a wide receptive field with two medium and three small length convolutional kernels. It also has high diagnostic accuracy and robustness when the environment is noisy and workloads are changed compared with other models. Furthermore, to demonstrate how the wide receptive field convolutional neural network model works, the reasons for high model performance are analyzed and the learned features are also visualized. Finally, the wide receptive field convolutional neural network model is verified by the vibration dataset collected in the background of high noise, and the results indicate that it has high diagnostic performance.

scholarly journals Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Fu ◽  
Qing Lv ◽  
Hsiung-Cheng Lin
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Fast Fourier Transform ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Batch Normalization ◽  
Fault Information

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

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