联合增量协作故障诊断方法用于多个风电场的动态数据流
IEEE transactions on cybernetics
|December 30, 2025
概括
风力轮机故障诊断的联合学习解决了动态数据流和新的故障类别. 拟议的方法减轻了内存退化,优于现有的方法,提高了可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 越来越多的隐私问题和数据孤岛需要用于风力轮机故障诊断的先进解决方案.
- 现有的联合学习方法与动态数据流和引入新的故障类作斗争,导致不切实际的存储和计算需求.
- 当前的模型在异质的,资源有限的风电场环境中诊断新故障时遭受了显著的内存退化.
研究的目的:
- 为跨多个风电场的动态数据流提出联合增量协作故障诊断方法.
- 为了应对新的故障类别检测,模型可塑性-稳定性平衡以及在动态环境中全球模型适应的挑战.
- 为了减轻内存退化,并提高风力轮机故障诊断中联合学习的性能.
主要方法:
- 一种新的故障类检测方法,用于识别新故障类的引入.
- 对于局部故障诊断模型的可塑性-稳定性平衡机制,以应对色的记忆问题.
- 全球模型适应性补偿方法,以解决由于数据异质性而导致的聚合模型内存退化.
主要成果:
- 提出的方法有效地减轻了风力轮机故障诊断的联合学习模型中的色记忆问题.
- 使用来自中国三座风电场的真实数据进行的验证显示,与最先进的方法相比,其性能优越.
- 该方法成功地处理动态数据流和新故障类的出现.
结论:
- 联合增量协作故障诊断方法为动态环境中的风力轮机维护提供了实用和有效的解决方案.
- 该研究强调了在工业应用中解决模型可塑性,稳定性和异质性的重要性,以实现强大的联合学习.
- 这项研究通过提高故障诊断能力,有助于提高风能的可靠性和效率.
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