A Review of Condition Monitoring and Fault Diagnosis of Wind Turbine Gearbox Using Signal Processing

2012 ◽  
Vol 608-609 ◽  
pp. 673-676 ◽  
Author(s):  
Zhi Qiang Xu ◽  
Jian Hua Zhang ◽  
Jing Fang Ji ◽  
Xiang Jun Yu

Due to gearbox is one of the high failure rate component in the wind turbine, the research of it has been paid wide attention in recent years. This paper reviewed the two aspects about the wind turbine gearbox. First, some signal process methods including how to determine the threshold were summarized. Then, the condition monitoring and fault diagnosis of gearbox were reviewed using the measured signals. These researches are benefited for reducing economic losses which is caused by the gearbox failure. Based on the above reviews, this paper gives some developmental direction.

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.


2012 ◽  
Vol 197 ◽  
pp. 206-210 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Jin Zhang ◽  
Shi Qing Wan

Wind power industry enormously expanded during the last several years. However, wind turbines are subjected to different sorts of failures, which lead to the increasement of the cost. The wind turbine gearbox is the most critical component in terms of high failure rates and long time to repair. This paper described common failures and root causes of wind turbine gearboxes. Then it focused on fault diagnosis and monitoring techniques for the wind turbine gearbox. The challenges and future research directions were presented, and the simulator rig of wind turbine gearbox was designed to develop condition monitoring and fault diagnosis techniques for wind turbine gearbox.


2019 ◽  
Vol 25 (12) ◽  
pp. 1852-1865 ◽  
Author(s):  
Vamsi Inturi ◽  
GR Sabareesh ◽  
K Supradeepan ◽  
PK Penumakala

Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. Two local bearing faults, namely, bearing inner race fault and bearing outer fault are seeded in the gearbox. The raw data from these techniques are decomposed and wavelet approximation coefficients of level four (a4) are extracted using discrete wavelet transform (DWT). A plethora of statistical features is computed from the wavelet approximation coefficients and the most significant features are being identified by implementing the decision tree algorithm. The classification efficiencies of each of these CM techniques are compared by using the support-vector machine algorithm. Furthermore, an integrated CM scheme is developed by combining the individual CM techniques and the fault diagnosing ability of the integrated CM scheme is compared with the individual CM techniques. A principal component analysis-based approach is used as a feature classification algorithm and an input feature matrix is formed by combining the significant features extracted from vibration, acoustic, and lubrication oil analysis. It has been observed that the integrated CM scheme has provided better classification interpretations than the single CM techniques and it can be extended for real time fault diagnosis of a wind turbine gearbox.


Author(s):  
Hongwei Xin ◽  
Xinjian Feng ◽  
Ye Xin ◽  
Ye Tian ◽  
Yutong Lin ◽  
...  

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.


Author(s):  
Jiatang Cheng ◽  
Yan Xiong

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.


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