基于动态建模和数据驱动表示的轮磨损故障的综合可解释诊断
Zemin Zhao1, Tianci Zhang2,3, Kang Xu2,3
1AECC Harbin Dongan Engine Co., Ltd., Harbin 150066, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了通过结合动态建模和深度学习来诊断轮磨损的新框架. 该方法准确地识别了轮磨损,为传动性能监测提供了可靠和可解释的解决方案.
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:
- 轮磨损显著影响传输性能和可靠性.
- 现有的故障诊断方法在解释性 (数据驱动) 或依赖先前知识 (动态模型) 上面临限制.
研究的目的:
- 开发一种可解释且准确的轮磨损诊断方法.
- 整合动态建模和深度学习,以增强故障检测.
主要方法:
- 建立了一个动态轮磨损模型来分析对网格硬度和振动的磨损影响.
- 采用深度学习网络与梯度加权类激活映射 (Grad-CAM) 进行特征可视化.
- 在动态模型和深度学习之间实施了双向验证框架.
主要成果:
- 综合框架准确地识别了磨损引起的调制效应,并增强了网格波.
- 在四种速度条件下实现了量化诊断指数,识别精度为0.9560,用于轮磨损.
- 与现有诊断指标相比,表现出优越的性能.
结论:
- 拟议的双向验证框架为轮磨损诊断提供了一种新,高度准确和可解释的方法.
- 这种方法通过有效的故障检测提高了传输系统的可靠性.
- 这些发现有助于推进机械系统的状态监测和预测性维护.
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