AI-driven bearing quality inspection and defect id
From: XingMao DATE: 2025/7/15 Hits: 5
AI-driven bearing quality inspection and defect id
AI-driven bearing quality inspection and defect recognition technology deeply integrates machine vision, deep learning algorithms and big data analysis, realizing automated high-precision screening of bearing appearance defects and intelligent prediction of potential faults, and promoting the intelligent upgrade of manufacturing quality inspection links. The...
AI-driven bearing quality inspection and defect recognition technology deeply integrates machine vision, deep learning algorithms and big data analysis, realizing automated high-precision screening of bearing appearance defects and intelligent prediction of potential faults, and promoting the intelligent upgrade of manufacturing quality inspection links. The following are its core technical points and application value:
1. Core functions and detection capabilities
A.Full coverage detection of appearance defects
Based on high-definition imaging and AI algorithms, the system can identify dozens of surface defects, including:
Geometric anomalies: dust cover collapse, cage crushing, outer ring chamfer is too large;
Surface damage: rust, vibration marks, pitting, scratches, cracks (crack depth up to 0.1mm);
Assembly defects: roller missing, nylon cage breakage, missing balls, etc.
The detection accuracy can reach micrometer level (μm), compatible with various types of bearings with a diameter of 20-500mm (cylindrical/Tapered Roller Bearings, etc.)
B. Online fault diagnosis and prediction
Through vibration signal analysis combined with intelligent sensors (such as impact pulse sensors):
Real-time monitoring of bearing operation status, quantification of DBM value (-9 to 99 range), abnormality when exceeding 20, and scrap risk when exceeding 31;
Adaptive signal decomposition technology (such as VMD, EWT) is used to increase the early crack detection rate to 91%;
The fusion of time series models (such as BiTCN) optimizes fault prediction, and the high-speed rail bearing diagnosis accuracy reaches 98.7%.
2. Technological innovation and performance breakthroughs
A.Algorithm optimization drives efficiency leap
Anti-interference imaging : For reflective surfaces (such as bearing rings), anti-reflective algorithms are used to solve edge grabbing and imaging quality problems;
Dynamic parameter optimization : The artificial bee colony algorithm (ABC) is used to optimize neural network parameters and improve model generalization capabilities;
Multi-objective analysis : Simultaneous defect identification, energy consumption monitoring and process improvement (such as reducing leakage rate by 40% + saving energy by 12%)
B.Detection efficiency and reliability
index
|
Performance Parameters
|
Technical Support
|
Detection speed
|
60 pcs/min (max. 750 pcs/hour)
|
High-speed image acquisition + parallel processing
|
Leakage rate
|
The leakage rate of large defects is 0
|
Multi-station cooperative verification mechanism
|
Over-detection rate
|
≤5%
|
Deep Learning False Judgement Suppression Algorithm
|
3. Application benefits and industrial value
A.Significant cost reduction and efficiency improvement
Replace manual quality inspection, reduce the labor cost of a single production line by 70%, and save 80,000-100,000 yuan per year ;
AI model development cycle is shortened by 90%, and the project is launched within three months .
B.Full process quality closed loop
Data is automatically saved (values + images), supporting quality traceability and statistical analysis ;
Seamless connection to the MES system to achieve a closed loop of production-quality inspection-improvement data flow
4. Development Trends
The technology is evolving towards multimodal fusion: combining vibration signals, infrared temperature monitoring (such as AIC6300 analyzer) and visual data to build a health portrait of the entire life cycle of the bearing; at the same time, edge computing deployment (such as real-time extraction of eigenvalues) further promotes the implementation of real-time warning capabilities. In the future, AI quality inspection will deeply empower flexible manufacturing and adapt to small-batch customized production scenarios.
