Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach
Condition monitoring and fault identification have become important aspects to ensure the proper operating condition of rotating machinery in industrial applications. In this sense, gearbox transmission systems and induction motors are important rotating elements due to they are probably the most used in industrial sites. Thus, from an industrial perspective, the occurrence of vibrations is inherent to the working condition in any rotating machine. To overcome this issue, condition monitoring strategies have to be developed aiming to avoid unnecessary cost and downtimes; thereby, condition-based maintenance based on vibration analysis has become as the most reliable approach with condition monitoring and fault identification purposes. In this regard, this work proposes a spectral analysis of the nonlinear vibration effects produced by worn gears and damaged bearings during the condition monitoring and fault assessment in an electromechanical system. The analysis is based on the spectral estimation from the available vibration and stator current signals; furthermore, the theoretical fault-related frequency components are estimated for being located in such estimated spectra. Consequently, the identification of different levels of uniform wear is performed by comparing the amplitude increase of those theoretical frequency components. Finally, through time-frequency maps is proved that an incipient fault, such as wear in gears and damage in bearings, may produce nonlinear frequency components that affect the proper operating condition of the electromechanical system. The proposed analysis is validated under a complete experimentally dataset acquired from a real laboratory electromechanical system.
KeywordsCondition monitoring Nonlinear vibrations Gearbox wear Damaged bearing Electromechanical systems
This proposed research has been partially supported by CONACyT under the Doctoral scholarship number 278033 and by FOFIUAQ-2018.
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