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Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR

机译:通过频谱峰度派生指标和SVR预测风力涡轮机高速轴轴承的健康状况

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摘要

A significant number of failures of wind turbine drivetrains occur in the high-speed shaft bearings. In this paper, a vibration-based prognostic and health monitoring methodology for wind turbine high-speed shaft bearing (HSSB) is proposed using a spectral kurtosis (SK) data-driven approach. Indeed, time domain indices derived from SK are used and a comparative study is performed with frequently used time-domain features in the bearing degradation health assessment. The effectiveness is quantified by two measures, i.e., monotonicity and trendability. Among those features, the area under SR is utilized for the first time as a condition indicator of rolling bearing fault. A support vector regression (SVR) model was trained and tested for the prediction of the HSSB lifetime prognostics, showing the superiority of SIC derived indices of degradation assessment. We verified the potential of the prognostics method using real measured data from a drivetrain wind turbine. The experimental results show that the proposed approach can successfully detect an early failure and can better estimate the degradation trend of HSSB than traditional time-domain vibration features. (C) 2017 Elsevier Ltd. All rights reserved.
机译:高速轴轴承中会发生大量的风力涡轮机传动系统故障。本文提出了一种基于频谱峰度(SK)数据驱动方法的基于振动的风力涡轮机高速轴轴承(HSSB)的预测和健康监测方法。确实,使用了从SK得出的时域指标,并在轴承退化健康评估中对经常使用的时域特征进行了比较研究。有效性通过两种方法量化,即单调性和趋势性。在这些特征中,SR下的面积首次用作滚动轴承故障的状态指标。对支持向量回归(SVR)模型进行了训练和测试,以预测HSSB的寿命预测,显示出SIC衍生的退化评估指标的优越性。我们使用来自传动系统风力涡轮机的实际测量数据验证了预测方法的潜力。实验结果表明,与传统的时域振动特征相比,该方法能够成功地检测出早期故障,并能更好地估计HSSB的退化趋势。 (C)2017 Elsevier Ltd.保留所有权利。

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