通过基于自编码器的神经网络和FMSA进行风力轮机故障检测
Welker Facchini Nogueira1, Arthur Henrique de Andrade Melani1, Gilberto Francisco Martha de Souza1
1Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School, University of Sao Paulo, Sao Paulo 05508-010, SP, Brazil.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究介绍了风力轮机的混合故障检测系统,将专家知识与人工智能结合起来,以早期预测故障. 这种方法提高了风电场的可靠性,并支持预测性维护.
科学领域:
- 可再生能源工程可再生能源工程
- 工业应用中的人工智能
- 状态监控系统 状态监控系统
背景情况:
- 风力发电对于清洁能源至关重要,但轮机的可靠性是关键.
- 现有的故障检测方法在复杂的系统中可能受到限制.
- 预测性维护对于优化风电场运营至关重要.
研究的目的:
- 为风力轮机开发一种新的混合故障检测方法.
- 将专家知识 (故障模式和症状分析) 与数据驱动模型 (自动编码器) 整合起来.
- 为了提高异常检测,特征选择和故障定位,以提高可靠性.
主要方法:
- 使用故障模式和症状分析 (FMSA) 来识别故障模式.
- 在健康的SCADA数据上训练的自编码神经网络.
- 使用重建错误和基于持久性的规则实施了异常检测策略.
- 采用故障特定的建模策略,用于定制的轮和故障模式分析.
主要成果:
- 在模拟数据上实现了99%的分类准确性.
- 在现实数据中报告失败的60天前成功检测到异常.
- 在变压器,变速箱,发电机和液压组等关键组件中发现了降解.
- FMSA集成改善了功能选择和故障定位.
结论:
- 混合方法显著提高了故障检测准确性和早期预警能力.
- 该系统提高了风力轮机状况监测的解释性和精度.
- 这种方法为风能系统的预测性维护提供了强大的解决方案.
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