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| Content Provider | IET Digital Library |
|---|---|
| Author | Liu, Sheng Sun, Yue Zhang, Lanyong |
| Abstract | This paper proposed a new method of fault diagnosis based on Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Fuzzy Recurrent Cerebellar Model Articulation Controller (FRCMAC) Neural Networks. Aiming at the problem that during the use of the NA-MEMD method, the white noise amplitude parameter needs to be selected by artificial experience, a method of using Genetic Algorithm (GA) to optimize its auxiliary white noise parameters is proposed, which facilitates the use of NA-MEMD. We proposed a novel FRCMAC structure which improved Learning efficiency and dynamic response speed than traditional CMAC structure. First, the GA-NA-MEMD method is applied to process the vibration signals of rolling bearings, and the signals are decomposed into a group of Intrinsic Mode Functions (IMFs). Then use energy moments of IMFs as fault feature vectors to train FRCMAC neural network, a neural network structure suitable for rolling bearing fault diagnosis is obtained. Finally, the data from bearing data center of Case Western Reserve University is used to prove that the fault diagnosis method proposed in this paper is superior to other methods in diagnosis time and precision, which can meet the training requirements more quickly with limited training samples and fault diagnosis results more accurate. |
| Starting Page | 107 |
| Ending Page | 113 |
| Page Count | 7 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 13, Jan (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/13 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8991 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2018-10-19 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Acoustic Signal Processing Artificial Experience Association Degree Association Unit Autoregressive Unit Auxiliary White Noise Parameter Bearing Data Center Bearing Fault Diagnosis Cerebellar Model Arithmetic Computers Diagnosis Time Digital Signal Processing Dynamic Mapping Fault Diagnosis Fault Diagnosis Approach Fault Diagnosis Method Fault Diagnosis Result Fault Feature Vector FRCMAC Neural Network FRCMAC Structure Fuzzy Processing Fuzzy Recurrent Cerebellar Model Articulation Controller Neural Network GA-NA-MEMD Method Gaussian Function Genetic Algorithm IMFs Input Space Intrinsic Mode Function Knowledge Engineering Technique Machine Bearings Mechanical Component Mechanical Engineering Mechanical Engineering Computing Neural Computing Technique Neural Network Structure Suitable Neurocontrollers Noise-assisted Multivariate Empirical Mode Decomposition Precision Rolling Bearings Signal Processing And Detection Statistics Traditional CMAC Structure Vibrations Vibrations And Shock Wave White Noise |
| Content Type | Text |
| Resource Type | Article |
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