基于多级极值梯度助推的风电齿轮箱故障智能诊断

Weixiong Jiang, Zhenqiao Zhu, W. Zhang, Limin Cheng, Zongzhen Ye, Jun Wu
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引用次数: 0

摘要

风力发电机齿轮箱因其优良的传动特性而广泛应用于风力发电机组中。风电齿轮箱的质量对风机的使用寿命有很大的影响。随着监测技术的发展,时域和频域分析作为一种记录风力发电机组运行状态的方法已经成熟。然而,对运行过程中的故障,特别是复合故障模式的识别是一个很大的挑战。在目前的工作中,提出了一种新的复合故障诊断方法——多级极值梯度增强(MsXGB),该方法可以同时诊断多个单独故障耦合的复合故障。诊断结果表明,测试准确率达97%,训练准确率达100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Fault Diagnosis of Wind Turbine Gearbox Based on Multi-stage Extreme Gradient Boosting
Wind turbine gearbox is widely used in wind power turbine due to its excellent transmission characteristics. The quality of wind turbine gearbox has great impact on the turbine life security. With the development of monitoring technology, as a method to record the operation state of wind power turbine, time-domain and frequency-domain analysis has been mature. However, it is of great challenge for human to identify the faults, especially compound failure pattern in operating processes. At present work, a novel compound fault diagnosis method called Multi-stage extreme Gradient Boosting (MsXGB) is proposed, which can diagnose compound faults coupled with multiple individual fault simultaneously. The diagnosis results show that the test accuracy is 97%, and the train accuracy is up to 100%.
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