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| Content Provider | IET Digital Library |
|---|---|
| Author | Liu, Xiuli Zhang, Xueying Luan, Zhongquan Xu, Xiaoli |
| Abstract | A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals. First, the rolling bearing signals are decomposed into intrinsic mode function (IMF) using EEMD. Then, the kurtosis of each component is calculated. Five components with large kurtosis are selected and the sample entropy is extracted to form the feature vectors. Finally, the feature vectors are input to the PNN for fault diagnosis. The method is used to classify the type of the rolling bearing fault. The results show that the accuracy of fault diagnosis of the proposed method is 100%, which proves the effectiveness of the proposed method. |
| Starting Page | 8696 |
| Ending Page | 8700 |
| Page Count | 5 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 23, Dec (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/23 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9086 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-03-25 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Civil And Mechanical Engineering Computing Digital Signal Processing Ensemble Empirical Mode Decomposition Sample Entropy Fault Diagnosis Hilbert Transform IMF Model Integral Transforms Kurtosis Model Maintenance And Reliability Mechanical Component Mechanical Engineering Mechanical Engineering Application of IT Neural Computing Technique Neural Nets Nonlinear Signal Nonsteady Signal Numerical Analysis Probabilistic Neural Network Probability Rolling Bearing Fault Diagnosis Method Rolling Bearings Statistics Vibrational Signal Processing Vibrations And Shock Wave |
| Content Type | Text |
| Resource Type | Article |
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