Abstract
The speed-up gearbox is one of key components in the large-sized wind turbine. During the operation, some faults often cause long maintenance downtime and higher cost. In this investigation, the gearbox faults were diagnosed by Bayesian Networks method. Based on the analysis of fault factors, the different signal features of fault diagnosis were confirmed. According to Bayesian Networks theory, the fault model of speed-up gearbox was established. The probability of sub-node was obtained by the conditional probability relationship of different nodes. Using the conditional independence of each node, and simplifying the probability distribution, the fault probability was counted out. Finally, the availability of Bayesian Networks method is proved by a calculation case on the test-platform. The study shows that the method can improve the fault diagnosis and operation level of the large-sized wind turbine when be used to judge the fault position in the gearbox.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, J.J., Wang, F.T., Dong, T.S.: Development of Online Monitoring System of Wind Turbine Gearbox. Industrial Control Computer 11, 49–50 (2009)
Hyers, R., McGowan, J., Sullivan, K., et al.: Condition Monitoring and Prognosis of Utility Scale Wind Turbines. Energy Materials 3, 187–203 (2006)
Thresher, R., Robinson, M., Veers, P.: The Status and Future of Wind Energy Technology. IEEE Power & Energy Magazine 5, 34–46 (2007)
Chen, C.Z., Liang, S.M.: Fault Diagnosis for Megawatt Wind Generator. Journal of Shenyang University of Technology 3, 277–280 (2009)
Jiang, D.X., Hong, L.Y., Huang, Q., et al.: Condition Monitoring and Fault Diagnostic Techniques for Wind Turbine. Power System and Clean Energy 3, 40–44 (2008)
Zhuang, Z.M., Yin, G.H., Li, F.L., et al.: Fault Diagnosis of Wind Power Generation Based on Wavelet Neural Network. Transactions of China Electro Technical Society 4, 224–228 (2009)
Li, J.J., Li, Y.: Anti-submarine Helicopter Identifying Submarine Target Model Based on Bayes Net. Ship Electronic Engineering 10, 54–56 (2009)
Carter, T.B.: Networks Inference, Error, and Informant (in) Accuracy: a Bayesian Approach. Social Networks 25, 103–140 (2003)
Lee, M.H., Choi, Y.H.: Fault Detection of Wireless Sensor Networks. Computer Communications 31, 3469–3475 (2008)
Liu, X.K., Liu, S.Q., Zhu, B.E., et al.: A Study of Failure Data Inference Based on Bayesian Network. Journal of Academy of Military Transportation 1, 70–73 (2008)
Sahin, F., Yavuz, M.C., Arnavut, Z., et al.: Fault Diagnosis for Airplane Engines Using Bayesian Networks and Distributed Particle Swarm Optimization. Parallel Computing 33, 124–143 (2007)
Chien, C.F., Chen, S.L., Lin, Y.S.: Using Bayesian Network for Fault Location on Distribution Feeder. IEEE Transactions on Power Delivery 13, 785–793 (2002)
Chris, J.N., James, R.B.: Inference in Bayesian Networks. Nature Biotechnology 1, 51–53 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, J., Hao, G. (2011). Research on the Fault Diagnosis of Wind Turbine Gearbox Based on Bayesian Networks. In: Wang, Y., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25658-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-25658-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25657-8
Online ISBN: 978-3-642-25658-5
eBook Packages: EngineeringEngineering (R0)