风力轮机叶片应变的长时间序列预测方法与增量Bi-LSTM学习
Bingkai Wang1, Wenlei Sun1, Hongwei Wang1
1School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究引入了一种增量预测方法,用于预测风力轮机叶片应变. 这种新的方法提高了结构健康监测和长期序列预测的准确性.
科学领域:
- 结构健康监测 结构健康监测
- 机器学习 机器学习
- 风能工程 风能工程
背景情况:
- 在风力负荷下预测风力轮机叶片 (WTB) 的长期应变状态存在重大挑战.
- 应变是WTB结构健康的关键指标,需要准确的长期预测模型.
研究的目的:
- 开发一种新的增量预测方法,用于在WTB中准确的长期菌株状况预测.
- 增强结构性健康监测预测模型的动态自我更新能力.
主要方法:
- 基于双向长短期记忆 (Bi-LSTM) 框架,建立了一个用于长时间序列预测的数学模型.
- 该方法通过错误监督反机制进行增量学习,用于动态参数更新.
- 经验重复和弹性重量巩固被整合在一起,以提高预测准确度.
主要成果:
- 与标准Bi-LSTM模型相比,提出的增量预测方法在准确度上有24%的改进.
- 这种方法比变压器模型的准确度提高了4.6%.
- 实验结果验证了增量学习方法对WTB菌株预测的有效性.
结论:
- 新的增量预测方法为长期预测风力轮机叶片健康状况提供了有效的解决方案.
- 这项研究为结构性健康监测中的长期序列预测提供了新的技术框架和理论基础.
- 动态自动更新机制提高了关键基础设施预测模型的稳定性和准确性.
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