基于转移学习的异质冷却机组故障诊断方法的研究
Qiaolian Feng1, Yongbao Liu1, Yanfei Li1
1College of Power Engineering, Naval University of Engineering, Wuhan 430030, China.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
本研究引入了一种新的深度传输学习方法,用于准确的冷却机组故障诊断,克服数据稀缺性和异质性挑战,以改善设备维护.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 冷却机组对于冷却系统至关重要,但获得足够的标记故障数据是具有挑战性的.
- 跨设备和操作条件的数据异质性阻碍了传统的数据驱动故障诊断.
- 由于数据限制,现有的方法难以准确识别故障.
研究的目的:
- 开发一种深度转移学习方法,用于冷却机组的准确故障诊断.
- 在实际应用中应对有限的标记数据和数据异质性的挑战.
- 为了使精确的小样本知识从源到目标领域的转移.
主要方法:
- 一种异质的转移学习方法,集成双通道自动编码器,域对抗训练和伪标签自我训练.
- 使用梯度反转层 (GRL) 和域区分器用于域不变特征提取.
- 使用来自目标领域的高可靠性伪标记样本进行联合培训.
主要成果:
- 拟议的方法在工业场景中实现了高故障诊断准确度.
- 在常规运行条件下有效识别各种冷却机组类型的常见故障.
- 在多类故障诊断任务中优于传统方法和现有的转移学习方法.
结论:
- 开发的方法允许精确的小样本知识传输用于冷却机组故障诊断.
- 为冷却系统的智能操作和维护提供了一个新的视角.
- 与现有技术相比,在精度和F1分数方面取得了显著的改进.
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