| Titre : | Wind turbine gearbox fault detection using multiple sensors with features level data fusion (2012) |
| Auteurs : | Y. Lu, Auteur ; Tang, J., Auteur ; H. Luo, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of engineering for gas turbines and power (Vol. 134 N° 4, Avril 2012) |
| Article en page(s) : | 08 p. |
| Note générale : | Génie mécanique |
| Langues : | Anglais |
| Index. décimale : | 620.1 (Essais des matériaux. Défauts des matériaux. Protection des matériaux) |
| Tags : | Accelerometers Acoustic emission Condition monitoring Fault diagnosis Feature extraction Gears Microphones Principal component analysis Sensor fusion Tachometers Time-frequency Vibrations Wind turbines |
| 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=JETPEZ000134000004042501000001&idtype=cvips&gifs=Yes&ref=no |
