Loading...
Please wait, while we are loading the content...
Research on Rolling Bearing Fault Identification Method Based on LSTM Neural Network
| Content Provider | Scilit |
|---|---|
| Author | Luo, Pan Hu, Yumei |
| Copyright Year | 2019 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering In order to simplify the fault detection process, improve the efficiency of fault detection and recognition accuracy, a rolling bearing fault recognition based on LSTM neural network is proposed. In this model, there is no need to perform any preprocessing on the original data. As long as the neural network model training is completed, the original signal can be detected and identified automatically by the model. In order to verify the performance of the model, the test results of the same fault data set are compared with the fault recognition model based on traditional machine learning. The results show that the fault recognition model based on LSTM neural network has obvious superior performance and higher recognition reliability. Its recognition accuracy rate reaches 98.00%, and the recognition accuracy of the fault recognition model based on traditional machine learning is only 94.20%. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/542/1/012048/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/542/1/012048 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 1 |
| Volume Number | 542 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-06-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Industrial Engineering Recognition Accuracy Recognition Model Based On Lstm Neural Network |
| Content Type | Text |
| Resource Type | Article |
