Gear early fault diagnosis method based on shock response and modulation mode synergy
By extracting the impact response and modulation mode features of early gear faults using a hybrid damped wavelet convolution kernel and a Gaussian filter kernel, and then synergistically fusing them, the problem of insufficient synergistic utilization of impact response and modulation mode in existing models is solved, thereby improving the accuracy and interpretability of early gear fault diagnosis.
Patent Information
- Application Number
- CN202610827067.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing deep learning models lack the synergistic utilization of impact response and modulation mode in early gear fault diagnosis, resulting in insufficient diagnostic accuracy and interpretability.
A method for diagnosing early gear faults is constructed by extracting the impact response features of early gear faults using a hybrid damped wavelet convolution kernel, extracting frequency domain modulation mode features using a Gaussian filter kernel, and then performing feature enhancement and adaptive weighted fusion through a collaborative fusion module.
It improves the accuracy, stability, and interpretability of early gear fault diagnosis, better characterizes early gear fault features, and enhances the model's ability to extract weak impact responses and frequency domain modulation modes.
Smart Images
Figure CN122409182A_ABST