一个混合深度学习框架用于机故障诊断
Muhammad Farooq Siddique1, Wasim Zaman1, Muhammad Umar1
1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的混合故障诊断框架,用于削切削工具,达到99.78%的准确性. 该系统有效地处理噪声信号和可变条件,以提供可靠的工业监控.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 工业监控面临的挑战包括噪音高的振动信号,有限的故障数据和操作变化.
- 精确的削工具故障诊断对于防止故障和优化生产至关重要.
研究的目的:
- 开发一个强大的混合故障诊断框架,用于削切削工具.
- 解决工业环境中噪音,有限数据和操作变化的挑战.
主要方法:
- 信号预处理以消除基线漂移并增强信号噪声比.
- 构建用于时间频率分析的对数连续波形波图.
- 应用Canny边缘运算符用于功能增强和文物抑制.
- 双分支编码器用于区分特征表示,捕获本地和远程依赖关系.
- 组合决策机制与验证指导权重,可靠的故障识别.
主要成果:
- 在实体切割工具数据的实验验证中获得了99.78%的准确性.
- 具有强大的环境噪声耐受性和在可变操作条件下一致的性能.
- 框架被证明是轻量级,可扩展和可用于实际工业应用的部署.
结论:
- 拟议的混合框架为高精度工具故障诊断提供了实用和有效的解决方案.
- 该方法成功克服了工业环境中噪音数据和有限的故障标签的局限性.
- 该系统确保在多样化和具有挑战性的条件下可靠,强大和稳定的故障识别.
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