Discussion and conclusion
Condition monitoring of wind turbines is an evolving field. Operators, researchers, and nations are highly focusing on improving wind turbine efficiency as a reliable replacement for the old polluting fossil fuel based electricity plants or dangerous nuclear plants. In order to reach an efficient system, it is vital to increase the lifetime expectancy of each component. Gearboxes cause a great share of the downtime of wind turbines when a fault occurs. Condition monitoring of gearboxes can be done through numerous and various approaches and techniques. Lubrication analysis is still the hardest to be applied since it relies on oil sample analysis or sensory systems. Oil sample analysis is done offline and might detect faults when it is too late. Moreover, sensory systems for debris detection are costly and require maintenance. On the other hand, several lubricant parameters can be indicators either for lubricant degradation (viscosity, humidity, etc.), or component failure (debris). The challenge in lubrication analysis come down to well chosen monitored parameters and the set of sensors to be installed responsible to monitor these parameters. Acoustic Emission analysis has been employed in all mechanical rotating systems. It is capable of detecting faults and lately localizing them. Since AE analysis deals with signals of high frequency, it is seen that signal processing techniques are used to extract signal features for gearbox fault detection in wind turbines. The main important and tedious challenge when using AE resides in the isolation of noises. Background internal and external noises interference may well render signal analysis useless for fault feature detection.
