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AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring

Abstract : This paper proposes an automatic procedure for condition monitoring. It represents a valuable tool for maintenance of expensive and spread systems such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic for the user. The paper briefly describes all the steps of the processing , from pre-processing of acquired signal to interpretation of generated results. It starts with an angular resampling method with speed measurement correction. Then comes a data validation step, in both time/angular and frequency/order domains. After these pre-processings, the spectral components of the analyzed signal are identified and classified in several classes from sine wave to narrow band components. This spectral peak detection and classification allows extracting the harmonic and side-band series which may be part of the signal spectral content. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, the full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked signal by signal. Finally, system health indicators are proposed to conclude about the condition of the investigated system. All mentioned steps create a self-sufficient tool for robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drive train.
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Submitted on : Monday, June 22, 2015 - 3:58:47 PM
Last modification on : Tuesday, June 14, 2022 - 12:12:49 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 7:17:35 PM

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  • HAL Id : hal-01166365, version 1

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Zhong-yang Li, Timothée Gerber, Marcin Firla, Pascal Bellemain, Nadine Martin, et al.. AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring. CM 2015 - MFPT 2015 - 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Jun 2015, Oxford, United Kingdom. ⟨hal-01166365⟩

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