Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data
Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data
复制标题
DOI：
10.3389/fenrg.2022.904622
复制
发表时间：
2022-05
影响因子：
4.6
通讯作者：
E. Latiffianti;S. Sheng;Yu Ding
中科院分区：
文献类型：
--
作者：
E. Latiffianti;S. Sheng;Yu Ding
风能行业正在不断改进其运营和维护实践，以降低能源成本。预测风力涡轮机的故障可以实现早期预警和及时干预，从而可以最大程度地防止昂贵的纠正性维护。它还避免了由于长期不可用而造成的生产损失。允许预警的一个关键要素是积累由风力涡轮机系统的逐渐退化引起的小幅度症状的能力。受累积和控制图方法的启发，本研究报告开发了具有这种预警能力的风力涡轮机故障检测方法。具体而言，解决了以下关键问题：积累什么故障信号，积累多长时间，使用什么偏移量，以及如何设置报警触发控制限值。我们应用所提出的方法，2年的监控和数据采集数据记录从五个风力涡轮机的价值。我们把我们的分析重点放在齿轮箱故障检测，其中所提出的方法表明，它能够预测故障事件具有良好的提前期。
The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.