基于CLAF-MTL特征深度集成的通用型液压泄漏诊断方法的研究
Chengbiao Tong1,2, Yu Xiong1, Xinming Xu1,2
1College of Electrical and Mechanical Engineering, Hunan Agricultural University, Changsha 410128, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种用于液压诊断的新型深度学习模型. CLAF-MTL模型准确预测泄漏率,并对故障类型进行分类,从而提高系统的安全性和效率.
科学领域:
- 机械工程 机械工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 液压对于系统的效率和安全至关重要.
- 目前的诊断方法缺乏多功能性和任务协调性.
- 现有研究的局限性阻碍了全面的门诊断.
研究的目的:
- 提出一个创新的多模式功能深度融合多任务预测 (CLAF-MTL) 模型.
- 为了解决用于液压诊断的多功能性和任务协调方面的局限性.
- 提高液压系统的诊断效率和实用性.
主要方法:
- 开发了一个CNN-LSTM-Additive Attention核心架构与一个完全连接的网络 (FCN).
- 实施了多任务学习机制,用于泄漏率回归和故障类型分类.
- 设计了一个互补的融合机制,整合了深度自动功能和多域功能.
主要成果:
- 在泄漏率预测方面获得了0.9784的R2.
- 在测试机组上达到92.23%的故障类型识别准确度.
- 在通用门场景中证明了卓越的稳定性和适用性.
结论:
- CLAF-MTL模型有效地整合了泄漏率预测和故障类型分类.
- 这种方法提供了双向任务互补性,克服了单一模式的局限性.
- 提供一种有效的解决方案,用于智能监控液压系统中门泄漏故障.
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