Rolling Bearing Fault Diagnosis Using Enhanced Convolutional Neural Network with Compressed Sensing

2018 ◽  
Author(s):  
Tianchen Liang ◽  
Jiayao Wang ◽  
Haoyu Wang ◽  
Shuaipeng Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Compressed Sensing ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 137395-137406 ◽  
Author(s):  
Laohu Yuan ◽  
Dongshan Lian ◽  
Xue Kang ◽  
Yuanqiang Chen ◽  
Kejia Zhai
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals A Review on Convolutional Neural Network in Bearing Fault Diagnosis

2019 ◽  
Vol 255 ◽  
pp. 06002 ◽  
Author(s):  
N. Fathiah Waziralilah ◽  
Aminudin Abu ◽  
M. H Lim ◽  
Lee Kee Quen ◽  
Ahmed Elfakharany
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Significant Contribution ◽  
Rolling Bearing ◽  
Accurate Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
New Challenges ◽  
Architecture Development

As the degradation of bearing yield to an enormous adverse impact on machinery and the damage that comes within could jeopardize human precious life. Hence, the bearing fault diagnosis is indisputably indispensable. This paper is predominantly focused on the utilization of Convolutional Neural Network (CNN) in bearing fault diagnosis of the rolling bearing. By deployment of CNN, an accurate diagnosis can be achieved without the necessity of pre-training the data. The function of CNN in diagnosing the bearing and architecture development of CNN are discussed. Lastly, to establish new and significant contribution in this area, new challenges are pinpointed.

scholarly journals Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 770 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Zuoyi Chen ◽  
Xuebing Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

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