Zhao Zemin, Zhang Tianci, Xu Kang, Tang Jinyuan, Yang Yudian
AECC Harbin Dongan Engine Co., Ltd., Harbin 150066, China.
State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.
Sensors (Basel). 2025 Aug 5;25(15):4805. doi: 10.3390/s25154805.
Gear wear degrades transmission performance, necessitating highly reliable fault diagnosis methods. To address the limitations of existing approaches-where dynamic models rely heavily on prior knowledge, while data-driven methods lack interpretability-this study proposes an integrated bidirectional verification framework combining dynamic modeling and deep learning for interpretable gear wear diagnosis. First, a dynamic gear wear model is established to quantitatively reveal wear-induced modulation effects on meshing stiffness and vibration responses. Then, a deep network incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) enables visualized extraction of frequency-domain sensitive features. Bidirectional verification between the dynamic model and deep learning demonstrates enhanced meshing harmonics in wear faults, leading to a quantitative diagnostic index that achieves 0.9560 recognition accuracy for gear wear across four speed conditions, significantly outperforming comparative indicators. This research provides a novel approach for gear wear diagnosis that ensures both high accuracy and interpretability.
齿轮磨损会降低传动性能，因此需要高度可靠的故障诊断方法。为了解决现有方法的局限性——动态模型严重依赖先验知识，而数据驱动方法缺乏可解释性——本研究提出了一种将动态建模和深度学习相结合的集成双向验证框架，用于可解释的齿轮磨损诊断。首先，建立了动态齿轮磨损模型，以定量揭示磨损对啮合刚度和振动响应的调制效应。然后，结合梯度加权类激活映射（Grad-CAM）的深度网络能够可视化提取频域敏感特征。动态模型和深度学习之间的双向验证表明，磨损故障中的啮合谐波增强，从而得出一个定量诊断指标，该指标在四种速度条件下对齿轮磨损的识别准确率达到0.9560，显著优于比较指标。本研究为齿轮磨损诊断提供了一种新方法，确保了高精度和可解释性。
