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An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model
| Content Provider | MDPI |
|---|---|
| Author | Chen, Chih-Cheng Liu, Zhen Yang, Guangsong Wu, Chia-Chun Ye, Qiubo |
| Copyright Year | 2020 |
| Description | The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads. |
| Starting Page | 59 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10010059 |
| Journal | Electronics |
| Issue Number | 1 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-31 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering 1d-cnn Fault Diagnosis Rolling Bearing Vibration Signal Single Load Different Loads |
| Content Type | Text |
| Resource Type | Article |
