Heavy-load large holding tool driving wheel multi-mode damage detection method based on machine vision

Through a multimodal damage detection method based on machine vision, combined with a multi-scale pyramid network and a cross-modal feature fusion network, the problem of insufficient feature extraction of heavy-loaded large-capacity driving wheel damage detection in the existing technology is solved, and damage detection and evaluation is not comprehensive enough, achieving higher accuracy and reliability.

CN119760646AActive Publication Date: 2025-04-04杨明川
9 Cites 19 Cited by

Patent Information

Application Number
CN202411937063.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-26
Publication Date
2025-04-04
Estimated Expiration
2044-12-26

AI Technical Summary

Technical Problem

In the damage detection of heavy-load large-load driving wheels, the problem of insufficient feature extraction, insufficient multi-source information fusion, and insufficient fine damage evaluation model, resulting in low detection accuracy and reliability.

Method used

Multimodal damage detection method based on machine vision is adopted to collect multi-source data through industrial cameras, vibration sensors and infrared sensors, and hierarchical feature extraction and feature fusion using multi-scale pyramid networks and cross-modal feature fusion networks. Combined with image segmentation, photometric stereoscopic method and vibration signal analysis, a quantitative evaluation model is constructed and information fusion is used to fusion with improved evidence theory.

Benefits of technology

It realizes more comprehensive and detailed detection of heavy-duty large-load driving wheel damage, improves detection accuracy and reliability, can identify and accurately locate damage in advance, provides detailed maintenance suggestions, reduces maintenance costs and improves the safety and reliability of the equipment.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a machine vision-based heavy-load large holding tool driving wheel multi-mode damage detection method, and relates to the technical field of automatic detection, and the method comprises the steps: collecting the image, vibration and temperature data of a driving wheel, extracting the visual features of the image through a multi-scale pyramid network, and obtaining the multi-mode damage of the driving wheel; inputting a cross-modal feature fusion network in combination with eccentricity and runout features of the vibration signals and stress concentration features of the temperature data to obtain fusion features; inputting the fusion features into a multi-task detection module to identify the damage, and measuring the damage area, depth, crack growth rate and stress concentration degree based on image segmentation, a photometric stereo method, vibration signal analysis and temperature gradient analysis; and finally, in combination with the equipment operation condition and the damage evolution law, an improved evidence theory is adopted to fuse the evaluation indexes, and an evaluation report including the damage state, the development trend and the maintenance suggestion is generated.
Need to check novelty before this filing date? Find Prior Art