Condition Monitoring of Wind Turbine Gearboxes Through On-site Measurement and Vibration Analysis Techniques

Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Luigi Garibaldi ◽  
Alessandro Paolo Daga
Author(s):  
Lu Yang ◽  
Lei Xie ◽  
Jie Wang ◽  
Dong Wang ◽  
Qiang Miao

As a type of clean and renewable energy source, wind power is growing fast as more and more countries lay emphasis on it. At the end of 2011, the global wind energy capacity reached 238 GW, with a cumulative growth of more than 20% per year, which is certainly a respectable figure for any industry. There is an exigent need to reduce the costs of operating and maintaining wind turbines while they became one of the fastest growing sources of power production in the world today. Gearbox is a critical component in the transmission system of wind turbine generator. Wind turbine gearbox operates in the extreme conditions of heavy duty, low speed and non-stationary load and speed, etc., which makes it one of the components that have high failure rate. To detect the fault of gearbox, many methods have been developed, including vibration analysis, acoustic emission, oil analysis, temperature monitoring, and performance monitoring and so on. Vibration analysis is widely used in fault diagnosis process and many efforts have been made in this area. However, there are many challenging problems in detecting the failure of wind turbine gearbox. The gearbox transforms low-speed revolutions from the rotor to high-speed revolutions, for example, from 20 rpm to 1500 rpm or higher. Usually one or more planetary gear stages are adopted in a gearbox design because the load can be shared by several planet gears and the transmission ratio can get higher. One disadvantage with the planetary gear stage is that a more complex design makes the detection and specification of gearbox failure difficult. The existing fault diagnosis theory and technology for fixed-shaft gearbox cannot solve the issues in the fault diagnosis of planetary gearbox. The planetary stage of wind turbine gearbox consists of sun gear, ring gear and several planet gears. The planet gears not only rotate around their own centers but also revolve around the sun gear center, and the distance between each planet gear to the sensor varies all the time. This adds complexity to vibration signals and results in difficulty in finding the fault-related features. The paths through which the vibration propagates from its origin to the sensors are complex, and the gears of other stage vibrate at the same time. This makes fault features be buried in noises. Further, the extreme conditions of heavy duty, low speed, and non-stationary workload lead to evidently non-stationary phenomena in the collected vibration. Methods to assess fault severity of a gearbox should be developed so as to realize fault prognosis and estimate of the remaining useful life of gearbox. Finally, other issues like signal analysis based on multi-sensor data fusion are also considered. This paper gives a comprehensive investigation on the state-of-the-art development in the wind turbine gearbox condition monitoring and health evaluation. The general situation of wind energy industry is discussed, and the research progresses in each aspects of wind turbine gearbox are reviewed. The existing problems in the current research are summarized in the end.


Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.


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