Abstract
Dynamic and static components health are important factors to keep large rotatory machine safety and efficient work. The shaft and bearing are key components in wind turbine drivetrain, whose health detection and diagnosis are important parts of wind turbine operation condition assessments. This paper proposed a novel failure diagnostics algorithm based on Gradient Boosting Decision Tree (GBDT) framework to recognize Acoustic Emission (AE) source in wind turbine drivetrain and further to diagnose components operational condition. This method combined Gradient Boosting (GB) with Decision Tree (DT) to improve the ability of network ensemble learning and enhance model generalization. The AE date sampled from the test rig of wind turbine drivetrain rig, are classified by fault type included normal, bearing crack and shaft rubbing. The experimental results indicate that the average recognition of these three fault conditions is approximated 97.43%. It is an effective method to recognize the rubbing faults for the machine normal operation.
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Acknowledgments
This work is supported by the National Nature Science Foundation of China (no. 51875100). The authors would like to thank anonymous reviewers and the associate editor, whose constructive comments help improve the presentation of this work.
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Qiang, C., Aidong, D., Jing, L., Yimeng, Z. (2019). Application of Gradient Boosting Decision Tree in Wind Turbine Drivetrain Recognition. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_53
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DOI: https://doi.org/10.1007/978-3-030-24274-9_53
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