18493391. DEFECT DETECTION USING MULTI-MODALITY SENSOR DATA simplified abstract (NEC Corporation)
Contents
DEFECT DETECTION USING MULTI-MODALITY SENSOR DATA
Organization Name
Inventor(s)
Yuncong Chen of Plainsboro NJ (US)
Wei Cheng of Princeton Junction NJ (US)
Haifeng Chen of West Windsor NJ (US)
Zhengzhang Chen of Princeton Junction NJ (US)
DEFECT DETECTION USING MULTI-MODALITY SENSOR DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18493391 titled 'DEFECT DETECTION USING MULTI-MODALITY SENSOR DATA
The patent application describes methods and systems for defect detection in a system by comparing predicted system states to actual system states.
- Determining a first residual score by comparing a predicted system state to an actual system state based on environment data.
- Determining a second residual score by comparing a predicted system state to an actual system state based on system state data.
- Generating a defect score based on the difference between the first and second residual scores.
- Performing an automatic action when the defect score indicates a defect in system behavior.
Potential Applications: - Quality control in manufacturing processes - Monitoring and maintenance of complex systems like machinery or equipment - Fault detection in automotive systems or electronic devices
Problems Solved: - Early detection of defects in systems - Improving system reliability and performance - Preventing system failures and downtime
Benefits: - Increased efficiency in identifying and addressing defects - Cost savings through proactive maintenance - Enhanced overall system reliability and longevity
Commercial Applications: "Advanced Defect Detection System for Industrial Machinery Maintenance and Quality Control"
Prior Art: There may be existing patents or research on predictive maintenance systems or fault detection algorithms in various industries.
Frequently Updated Research: Stay updated on advancements in predictive analytics, machine learning algorithms, and IoT technologies related to defect detection systems.
Questions about the technology:
Question 1: How does this defect detection system compare to traditional manual inspection methods? Answer: This system automates the defect detection process, providing real-time analysis and proactive maintenance, unlike manual inspections that are time-consuming and less accurate.
Question 2: Can this technology be integrated into existing systems or is it a standalone solution? Answer: This technology can be integrated into existing systems, enhancing their capabilities for defect detection and predictive maintenance.
Original Abstract Submitted
Methods and systems for defect detection include determining a first residual score by comparing a first predicted system state, determined according to previously measured environment data, to an actual system state. A second residual score is determined by comparing a second predicted system state, determined according to previously measured system state data, to the actual system state. A defect score is generated based on a difference between the first residual score and the second residual score. An automatic action is performed responsive to a determination that the defect score indicates a defect in system behavior.
