A Novel Approach to Fault Diagnosis of High Voltage Transmission line - A Self Attentive Convolutional Neural Network Model

2020 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Md. Rabiul Islam Sarker ◽  
Md. Arifuzzaman ◽  
Md. Sakhawat Hosen ◽  
Subrata K Sarker ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Line ◽  
Convolutional Neural Network ◽  
Network Model ◽  
High Voltage ◽  
Neural Network Model ◽  
High Voltage Transmission Line ◽  
Novel Approach ◽  
High Voltage Transmission

scholarly journals Comparisons of different deep learning-based methods on fault diagnosis for geared system

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816 ◽  
Author(s):  
Bing Han ◽  
Xiaohui Yang ◽  
Yafeng Ren ◽  
Wanggui Lan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Mechanical System ◽  
Network Model ◽  
Learning Theory ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Gear Fault

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

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.

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