基于TROA-SVM的风力轮机叶片故障检测方法
Zhuo Lei1, Haijun Lin1, Xudong Tang1
1College of Engineering and Design, Hunan Normal University, Changsha 410081, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种使用Tyrannosaurus优化算法 (TROA) 和支持矢量机器 (SVM) 的新方法,用于从噪音信号中检测风力轮机叶片故障. TROA-SVM模型实现了98.7%的准确性,超过了传统方法.
科学领域:
- 工程 工程师 工程师 工程师
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 风力轮机在恶劣的环境中运行,导致叶片疲劳损伤.
- 有效的监测和维护对于轮机的寿命和运行至关重要.
- 现有的故障检测方法在复杂的条件下可能缺乏准确性.
研究的目的:
- 开发一种使用声信号的风力轮机叶片的新型故障检测方法.
- 通过先进的算法来提高故障检测的准确性和可靠性.
- 为应对风力轮机叶片环境因素所带来的挑战.
主要方法:
- 来自运行中的风力轮机的原始噪声数据的信号预处理.
- 从时间,频率和 cepstral 领域提取特征.
- 整合龙优化算法 (TROA) 与支持矢量机器 (SVM) 进行分类.
主要成果:
- 构建了一个全面的特征矩阵,捕捉多维特征.
- 进行特征选择,以保留最重要的数据.
- 该TROA-SVM模型实现了98.7%的识别准确率.
结论:
- 拟议的TROA-SVM方法在风力轮机叶片故障检测方面表现出卓越的有效性.
- 这种方法在SVM,KNN和随机森林等传统方法上显著改进.
- 该方法为监测和维护风力轮机健康提供了可靠的解决方案.
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