Binocular Gear Pitting Detection Using Deep Learning
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Solution Overview
Problem
Current gear pitting detection methods rely on human observation, resulting in low efficiency, accuracy, and a lack of scientific, intelligent, and quantitative evaluation, failing to effectively prevent gear tooth breakage due to the absence of reliable automated detection systems.
Innovation Solution
A binocular automatic gear pitting detection device based on deep learning, comprising a gearbox, data acquisition system, image processing system, and tooth surface positioning system, uses CCD industrial cameras and infrared detection to acquire and process images, applying deep learning for quantitative evaluation and grading of gear pitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human eyes are used for gear pitting detection, then the detection can be performed with simple equipment, but the detection efficiency and accuracy are low
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an automated optical detection system consisting of CCD industrial cameras, image processing systems, and deep learning algorithms. This substitution eliminates manual observation and enables automated, high-precision detection of gear pitting, simultaneously improving both detection accuracy and efficiency.
2Measurement precision
If human observation method is used for gear pitting detection, then no complex detection system is needed, but quantitative evaluation and scientific detection are lacking
Solution Approach 1:
The patent replaces subjective human observation with an objective automated detection system that captures images via CCD cameras, processes them through image processing algorithms, and performs quantitative evaluation using deep learning models. This enables scientific, data-driven assessment of gear pitting conditions.
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the gear pitting phenomenon and the evaluation result. The system captures images of gear surfaces, processes them through algorithms, and generates quantitative assessments, serving as a mediator that transforms physical pitting into measurable data.
3Productivity
If automated detection system is introduced, then detection efficiency and accuracy are improved, but the system complexity increases
Solution Approach 1:
The patent designs the detection system with multi-functional components that perform multiple operations. The image processing system simultaneously captures images, processes them, identifies pitting features, and generates quantitative evaluations. This consolidation of functions into integrated modules improves efficiency while managing system complexity through functional integration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device achieves precise and intelligent quantitative evaluation of gear pitting, effectively preventing gear tooth breakage by identifying and grading different forms of pitting, improving detection accuracy and efficiency while reducing human intervention.
Implementation Method 1
an infrared detection device for acquiring reflector information... the infrared detection system obtains the moving position information of the gear teeth through symmetrically placed infrared transmitter, infrared receiver and the reflectors
Implementation Method 2
the infrared detection system obtains the moving position information of the gear teeth through symmetrically placed infrared transmitter, infrared receiver and the reflectors
Implementation Method 3
a CCD industrial camera is arranged on the data acquisition system; by adjusting the shooting position and the shooting angle of the CCD industrial camera, the image data in a gear pitting process is acquired
Data Source
AI summary
The present invention belongs to the field of computer visual detection, and relates to a binocular automatic gear pitting detection device based on deep learning, comprising a gearbox system, a data acquisition system, an image processing system, a tooth surface positioning system, a control system and a motor, wherein the gearbox is used for installing paired meshing gears; the data acquisition system is arranged on the side wall of the gearbox, and a CCD industrial camera is arranged on the data acquisition system; the image processing system completes quantitative evaluation of gear pitting and target detection based on a deep learning technology; both ends of the tooth surface positioning system are respectively connected with the motor and the gearbox, and the torque of the motor is transmitted to an input shaft of the gearbox. The device can determine the optimal installation base points of the data acquisition system according to the characteristics of the meshing gears, and find effective detection areas in combination with the light source and camera arrangement solutions, thereby effectively saving the installation space of the detection device and adapting to the operating characteristics of the meshing gears.