# Integrated Explainable Diagnosis of Gear Wear Faults Based on Dynamic Modeling and Data-Driven Representation

## TL;DR

This paper introduces a new method for diagnosing gear wear by combining dynamic modeling and deep learning to achieve both high accuracy and interpretability.

## Contribution

The novel contribution is an integrated bidirectional verification framework that combines dynamic modeling with interpretable deep learning for gear wear diagnosis.

## Key findings

- The dynamic model reveals wear-induced modulation effects on meshing stiffness and vibration responses.
- The deep learning model with Grad-CAM achieves 0.9560 recognition accuracy for gear wear across four speed conditions.
- Bidirectional verification enhances meshing harmonics in wear faults and provides a quantitative diagnostic index.

## Abstract

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.