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Wind turbine gearbox fault detection using multiple sensors with features level data fusion / Y. Lu in Transactions of the ASME . Journal of engineering for gas turbines and power, Vol. 134 N° 4 (Avril 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 4 (Avril 2012) . - 08 p. Titre : Wind turbine gearbox fault detection using multiple sensors with features level data fusion Type de document : texte imprimé Auteurs : Y. Lu, Auteur ; Tang, J., Auteur ; H. Luo, Auteur Année de publication : 2012 Article en page(s) : 08 p. Note générale : Génie mécanique Langues : Anglais (eng) Mots-clés : Accelerometers  Acoustic  emission  Condition  monitoring  Fault  diagnosis  Feature  extraction  Gears  Microphones  Principal  component  analysis  Sensor  fusion  Tachometers  Time-frequency  analysis  Vibrations  Wind  turbines Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Fault detection in complex mechanical systems such as wind turbine gearboxes remains challenging, even with the recently significant advancement of sensing and signal processing technologies. As first-principle models of gearboxes capable of reflecting response details for health monitoring purpose are difficult to obtain, data-driven approaches are often adopted for fault detection, identification or classification. In this paper, we propose a data-driven framework that combines information from multiple sensors and fundamental physics of the gearbox. Time domain vibration and acoustic emission signals are collected from a gearbox dynamics testbed, where both healthy and faulty gears with different fault conditions are tested. To deal with the nonstationary nature of the wind turbine operation, a harmonic wavelet based method is utilized to extract the time-frequency features in the signals. This new framework features the employment of the tachometer readings and gear meshing relationships to develop a speed profile masking technique. The time-frequency wavelet features are highlighted by applying the mask we construct. Those highlighted features from multiple accelerometers and microphones are then fused together through a statistical weighting approach based on principal component analysis. Using the highlighted and fused features, we demonstrate that different gear faults can be effectively detected and identified. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000004 [...] [article] Wind turbine gearbox fault detection using multiple sensors with features level data fusion [texte imprimé] / Y. Lu, Auteur ; Tang, J., Auteur ; H. Luo, Auteur . - 2012 . - 08 p. Génie mécanique Langues : Anglais (eng) in Transactions of the ASME . Journal of engineering for gas turbines and power > Vol. 134 N° 4 (Avril 2012) . - 08 p. Mots-clés : Accelerometers  Acoustic  emission  Condition  monitoring  Fault  diagnosis  Feature  extraction  Gears  Microphones  Principal  component  analysis  Sensor  fusion  Tachometers  Time-frequency  analysis  Vibrations  Wind  turbines Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Fault detection in complex mechanical systems such as wind turbine gearboxes remains challenging, even with the recently significant advancement of sensing and signal processing technologies. As first-principle models of gearboxes capable of reflecting response details for health monitoring purpose are difficult to obtain, data-driven approaches are often adopted for fault detection, identification or classification. In this paper, we propose a data-driven framework that combines information from multiple sensors and fundamental physics of the gearbox. Time domain vibration and acoustic emission signals are collected from a gearbox dynamics testbed, where both healthy and faulty gears with different fault conditions are tested. To deal with the nonstationary nature of the wind turbine operation, a harmonic wavelet based method is utilized to extract the time-frequency features in the signals. This new framework features the employment of the tachometer readings and gear meshing relationships to develop a speed profile masking technique. The time-frequency wavelet features are highlighted by applying the mask we construct. Those highlighted features from multiple accelerometers and microphones are then fused together through a statistical weighting approach based on principal component analysis. Using the highlighted and fused features, we demonstrate that different gear faults can be effectively detected and identified. DEWEY : 620.1 ISSN : 0742-4795 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JETPEZ000134000004 [...]