The New Standard in Non-Destructive Testing
MA (Motion Amplification) is a non-destructive inspection method that utilizes high-speed cameras and advanced image processing technology to visually amplify and analyze subtle movements and vibrations of structures and equipment. This technique precisely diagnoses defects, imbalances, and excessive vibrations in machinery and structures, maximizing their safety and performance. By effectively monitoring equipment conditions, detecting potential failures in advance, and improving maintenance efficiency, it contributes to extending equipment lifespan and preventing unexpected accidents
Vision
Shaping the Future of Diagnostics with Motion Amplification Technology
Our goal is to maximize equipment safety and revolutionize the efficiency of diagnostics and monitoring through Motion Amplification (MA) technology. By integrating high-speed image processing with AI-based data analytics, we are overcoming the limitations of traditional diagnostic methods and delivering more reliable and visually compelling analysis results to our customers. Through continuous research and development, we are enhancing the precision and expanding the applicability of MA technology, leading the future of non-destructive testing and equipment diagnostics.
R&D
Development of AI-Based Data Interpretation Algorithms
Key Research Areas
1. Automatic Classification of Abnormal Vibration Patterns in MA Data
• Implementation of an Algorithm for Detecting Abnormal Vibration Patterns in Specific Frequency Bands
2. Development of Customized Data Analysis Models for Each Equipment Type
• Development of Optimization Algorithms for Data Interpretation Based on Structural Characteristics of Equipment
3. Implementation of Lightweight AI Models for Real-Time Analysis
• Development of Lightweight Models for Real-Time Data Processing
• Rapid Anomaly Diagnosis and Feedback Provision
Dynamic Condition Monitoring of Structures and Machinery
Key Research Areas
1. Frequency Band-Based Analysis for Equipment Defect Diagnosis
• Decomposition of Frequency Bands and Analysis of Correlations with Defects
• Prediction of Defect Location and Severity through Frequency Feature Mapping
2. Development of a Structural Health Monitoring System
• Tracking Structural Health Status Based on Collected Data
