The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.

中文翻译：

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基于物联网的风力发电机驱动系统疲劳寿命预测方法

风力涡轮机驱动系统是将风能转化为电能的关键组件之一。驱动系统的寿命预测对于风机的维护非常重要。随着容量的增加，风力涡轮机系统变得更加复杂。因此，对于驱动系统的寿命预测，有必要考虑大数据的多信息融合，时变动态载荷的量化以及多损伤耦合分析等问题。为了解决上述挑战，提出了一种考虑了多重损伤耦合作用的疲劳寿命分析与评估方法。引入了具有故障物理技术的分级贝叶斯理论，以解决风力发电机驱动系统的不确定性问题。然后，建立了基于多损伤耦合竞争失效机理的时变性能分析模型。此外，引入了物联网（IoT）技术并将其与提出的模型相结合。通过物联网的数据采集，可以获得驱动系统的时间应力曲线。以驱动系统剩余疲劳寿命估算为例，说明了该方法的有效性。