| description abstract | M on one hand by developing new turbine designs and on the other hand by adopting condition monitoring approaches. One of the most critical and rather complex assemblies of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset's lifetime, according to the IEC 61400-4 standards. On the other hand, a recent study over approximately 350 offshore wind turbines indicated that gearboxes might have to be replaced as early as 6.5 years. Therefore a plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault. Among others, envelope analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the fault. Sometimes the gearbox is equipped with many acceleration sensors and its kinematics is clearly known. In these cases cyclostationary analysis and the corresponding methodologies, i.e., the cyclic spectral correlation and the cyclic spectral coherence, have been proposed as powerful tools. On the other hand often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy (permutation entropy, approximate entropy, samples entropy, fuzzy entropy, conditional entropy, and Wiener entropy), on Negentropy (Infogram), on Sparsity (Sparse-L2/L1, Sparse-L1/L0, and Sparse-Gini index) and on Statistics (mean, standard deviation, kurtosis, etc.). The performance of the indicators is evaluated and compared on a wind turbine dataset, consisted of vibration data captured by one accelerometer mounted on six 2.5 MW wind turbines, located in a wind park in northern Sweden, where two different bearing faults have been filed, for one wind turbine, during a period of 46 months. Among the different diagnostic indicators permutation entropy, approximate entropy, samples entropy, fuzzy entropy, conditional entropy, and Wiener entropy achieve the best results detecting blindly the two failure events. | |
