动态条件对抗性适应用于风力轮机变速箱故障诊断
Hongpeng Zhang1, Xinran Wang1, Cunyou Zhang1
1Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究提出了一种新的动态条件对抗领域适应模型,以改善在不同操作条件下改善风力轮机变速箱故障诊断. 该方法通过动态调整数据分布和优先考虑可转移样本来提高准确性和稳定性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 深度学习模型在风力轮机变速箱故障诊断方面遇到困难,原因是运行条件和数据分布的变化.
- 准确的故障诊断对于风力轮机的可靠性和运营效率至关重要.
研究的目的:
- 为风力轮机变速箱开发一个强大的故障诊断模型,以克服交叉条件数据差异带来的挑战.
- 在存在多样化的操作数据的情况下,提高故障诊断的准确性和稳定性.
主要方法:
- 引入了一个动态的条件对抗域适应模型,结合了距离度数因子来调整分布对齐.
- 综合信息参数,以评估和优先考虑域适应期间的样本可转移性.
- 验证了模型在实验装备和轴承数据集上的性能.
主要成果:
- 与现有的先进转移学习技术相比,拟议的模型在交叉条件故障诊断中表现出卓越的准确性和稳定性.
- 分布对齐和样本优先级的动态调整在处理各种数据分布时被证明是有效的.
- 在多重传输故障诊断任务中取得了显著的改进.
结论:
- 动态条件对抗域适应模型为可靠的风力轮机变速箱故障诊断提供了一个有希望的解决方案.
- 该方法对不同操作条件的适应性使其非常适合于现实世界的风能应用.
- 这种方法提高了深度学习在风力轮机条件监测中的实际适用性.
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