Induction machine bearing fault diagnosis based on the axial vibration analytic signal
| Autor: | Youcef Soufi, Ammar Medoued, Mourad Mordjaoui, Djamel Sayad |
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| Rok vydání: | 2016 |
| Předmět: |
Test bench
Renewable Energy Sustainability and the Environment Feature vector 020208 electrical & electronic engineering Energy Engineering and Power Technology Particle swarm optimization 02 engineering and technology 021001 nanoscience & nanotechnology Condensed Matter Physics Vibration symbols.namesake Fuel Technology Time–frequency representation Control theory 0202 electrical engineering electronic engineering information engineering symbols Hilbert transform Analytic signal 0210 nano-technology Induction motor Mathematics |
| Zdroj: | International Journal of Hydrogen Energy. 41:12688-12695 |
| ISSN: | 0360-3199 |
| DOI: | 10.1016/j.ijhydene.2016.02.116 |
| Popis: | This paper deals with a new induction motor defects diagnosis using the Axial Vibration Analytical Signal (AVAS). The signal is generated by a bearing-defected induction machine. The calculation method may be divided into two main parts; the former is the Hilbert transform that consists in the first part normalization of the axial vibration and its comparison with the AVAS module. The second part consists in the extraction of feature vectors using the Signal Class Dependent Time Frequency Representation ( T F R S C D ) based on the Fisher contrast design of the non parametrically kernel. The Particle Swarm Optimization (PSO) is used to optimize the feature vectors size. The vibration severity caused by the bearing fault is investigated for different loads. This last decreases with the increasing level of the load. The obtained results are experimentally validated on a 5500 W induction motor test bench. |
| Databáze: | OpenAIRE |
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