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| Fault Diagnosis of Rotor System Based on BP Neural Network and Sugeno Fuzzy Integral |
| Received:April 04, 2019 Revised:May 16, 2019 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2019.10.019 |
| KeyWord:neural network fuzzy integral rotor system axis trajectory fault diagnosis |
| Author | Institution |
| TIAN Fu-guo |
College of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an , China |
| WANG Qing-hua |
College of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an , China |
| JIA Kang |
College of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an , China |
| DENG Dong-hua |
Instrument Automation Room, China National Petroleum Pipeline Engineering Co., Ltd, Langfang , China |
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| Abstract: |
| Objective To propose a diagnosis method of rotor fault axis trajectory recognition based on Sugeno fuzzy integral and BP neural network to address the limitations of BP neural network for rotor fault diagnosis. Methods Firstly, the invariant moment of the axial trajectory image was used as the feature vector to extract the fault characteristics of common rotating machinery rotors. Then, multiple BP neural networks were used to identify the fault types. Finally, the Sugeno fuzzy integral was used to make decisions on the BP neural network recognition results, construct a rotor fault diagnosis model and apply it to diagnose faults of the rotor system. Results The six common rotor system fault signals were collected by the mechanical fault simulation platform. The matlab2012a software was used for programming, modeling and simulation. The test showed that the model effectively improved the recognition accuracy of multi-class faults in the rotor system. At the same time, the method required fewer samples for identifying the same fault type, which greatly saved data acquisition and processing time. Conclusion This method is proposed for fault diagnosis of rotor system. It has high diagnostic accuracy and high reliability. It requires less sample data and saves time. It has a good effect on fault diagnosis of small sample data. |
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