基于AFS和信号分析理论的海上风力轮机变速箱可解释的故障诊断框架
Hanlin Liu1, Xin Wang2, Hongpeng Zhang3
1Modern Industry College, Jilin Jianzhu University, Changchun 130118, China; Marine Engineering College, Dalian Maritime University, Dalian 116026, China.
ISA transactions
|August 15, 2025
概括
本研究引入了海上风力轮机变速箱的可解释故障诊断框架,实现了100%的准确性. 这种新方法提高了关键旋转机械状态监测的透明度.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 人工智能的人工智能
背景情况:
- 海上风力轮机变速箱故障诊断对于运营效率和降低成本至关重要.
- 现有的数据驱动方法往往缺乏解释性,阻碍了对诊断过程的信任和理解.
- 对于关键工业设备的透明和准确故障检测的需求至关重要.
研究的目的:
- 为海上风力轮机变速箱开发一个可解释的故障诊断框架.
- 提高故障诊断计算过程的透明度和可理解性.
- 为了保持高故障识别准确度,同时提高模型的解释性.
主要方法:
- 提出了一个可解释的故障诊断框架 (AFSBWFA),集成了公理模糊集 (AFS) 理论和信号分析.
- 开发了一种信号消除和重建方法 (BWF),结合了黑算法 (BKA),波段包分解 (WPD) 和特征模式分解 (FMD).
- 使用多尺度模糊 (MFE) 和基于AFS的分类器 (AFSCC) 设计了一种二维时间频率特征提取方法 (MFETF).
主要成果:
- AFSBWFA框架在私人 (DMU) 和公共 (BJU) 数据集上实现了100%的诊断准确性.
- 证明了出色的解释性,优于浅层机器学习,并实现与深度学习方法相匹配的性能.
- 该框架包含丰富的语义信息,为变速箱故障诊断提供了新的技术参考.
结论:
- 拟议的可解释故障诊断框架有效地解决了传统方法的局限性.
- AFSBWFA为海上风力轮机变速箱状况监测提供了一个透明和高度准确的解决方案.
- 这种方法为未来的智能故障诊断研究提供了宝贵的技术参考.
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