Neural Network and Fuzzy Logic Diagnostics of 1x Faults in Rotating Machinery
| Autor: | Yasser Zeyada, T. A. F. Hassan, A. K. Soliman, Aly El-Shafei, N. Rieger |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Learning vector quantization
Engineering Adaptive neuro fuzzy inference system Artificial neural network Neuro-fuzzy Series (mathematics) business.industry Mechanical Engineering Test rig Energy Engineering and Power Technology Aerospace Engineering Control engineering Fuzzy logic Fuzzy electronics ComputingMethodologies_PATTERNRECOGNITION Fuel Technology Nuclear Energy and Engineering Fuzzy inference engine ComputingMethodologies_GENERAL business |
| Zdroj: | Journal of Engineering for Gas Turbines and Power. 129:703-710 |
| ISSN: | 1528-8919 0742-4795 |
| Popis: | In this paper, the application of neural networks and fuzzy logic to the diagnosis of faults in rotating machinery is investigated. The learning-vector-quantization (LVQ) neural network is applied in series and in parallel to a fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig (Hassan et al., 2003, ASME Paper No. GT 2003-38450), and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated. |
| Databáze: | OpenAIRE |
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