Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes
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基于改进粒子滤波的风电传动齿轮箱轴承剩余使用寿命预测

DOI:
10.1109/tie.2018.2866057
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发表时间:
2019-06-01
影响因子:
7.7
通讯作者:
Hao, Liwei
Hao, Liwei
中科院分区:
计算机科学1区
文献类型:
--
作者:
Cheng, Fangzhou;Qu, Liyan;Hao, Liwei

文献摘要

被引文献

相似文献

轴承是风力涡轮机齿轮箱故障的主要原因。准确预测风电机组传动齿轮箱的剩余使用寿命,对于实现风电机组的状态检修,提高风电涡轮机的可靠性,降低风电成本具有重要意义。然而,由于监测数据有限和缺乏准确的物理故障退化模型,剩余使用寿命预测是一项具有挑战性的工作。粒子滤波方法已被用于风力涡轮机传动系统齿轮箱的剩余使用寿命预测,但由于粒子多样性低,这可能会导致不令人满意的预测结果,遭受粒子泛化问题。针对这一问题,本文提出了一种改进的粒子滤波算法,该算法设计了一个自适应神经模糊推理系统,利用从监测数据中提取的故障指标,学习故障退化模型中的状态转移函数;提出了一种粒子修正方法和一种改进的多项式重构方法,以提高重构过程中粒子的多样性,解决粒子的重构问题问题.增强粒子滤波算法成功地应用于预测轴承的剩余使用寿命的传动系统齿轮箱的2.5 MW风力涡轮机配备了双馈感应发电机。
Bearing is the major contributor to wind turbine gearbox failures. Accurate remaining useful life prediction for drivetrain gearboxes of wind turbines is of great importance to achieve condition-based maintenance to improve the wind turbine reliability and reduce the cost of wind power. However, remaining useful life prediction is a challenging work due to the limited monitoring data and the lack of an accurate physical fault degradation model. The particle filtering method has been used for the remaining useful life prediction of wind turbine drivetrain gearboxes, but suffers from the particle impoverishment problem due to a low particle diversity, which may lead to unsatisfactory prediction results. To solve this problem, this paper proposes an enhanced particle filtering algorithm in which an adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resampling process to solve the particle impoverishment problem. The enhanced particle filtering algorithm is applied successfully to predict the remaining useful life of a bearing in the drivetrain gearbox of a 2.5 MW wind turbine equipped with a doubly-fed induction generator.