探索风力轮机早期预测性维护的局限性,应用异常检测技术
Mindaugas Jankauskas1, Artūras Serackis1, Martynas Šapurov1,2
1Department of Computer Science and Communications Technologies, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究使用反复的神经网络来检测风力轮机变速箱温度的异常,预测到37天前的故障. 早期异常检测可以提高关键风能组件的预测性维护.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 可再生能源系统可再生能源系统
背景情况:
- 风力轮机的故障,特别是像变速箱这样的关键部件的故障,可能会导致大量的停机时间和成本.
- 预测性维护策略对于优化风能基础设施的运行寿命和可靠性至关重要.
- 准确早期检测操作参数中的异常是防止灾难性故障的关键.
研究的目的:
- 为了调查设备参数出现异常和关键组件故障之间的时间间隔.
- 开发和评估用于风力轮机变速箱早期异常检测的循环神经网络模型.
- 评估温度时间序列建模用于预测即将发生的故障的有效性.
主要方法:
- 利用循环神经网络 (RNN) 来建模健康风力轮机的时间序列数据.
- 通过将预测参数值与实际测量值进行比较,实现异常检测.
- 在风力轮机出现故障的SCADA数据上训练并测试了RNN模型,专注于变速箱温度.
主要成果:
- 在组件故障前37天,风力轮机变速箱温度的异常被成功检测到.
- 该研究比较了温度数据的各种时间序列建模方法.
- 分析了不同输入特征对温度异常检测性能的影响.
结论:
- 循环神经网络是用于在关键风力轮机组件中早期检测异常的有效工具.
- 预测变速箱温度异常提供了显著的干预时间,提高了预测性维护.
- 这些发现支持集成先进的机器学习技术,以提高风能系统的可靠性.
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