AI Image Diagnosis for Real-Time Battery Production Abnormalities
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Solution Overview
Problem
Existing factory monitoring systems for battery production lack real-time process management and have limitations in diagnostic accuracy, particularly in identifying abnormalities in battery cells or assemblies using video-based monitoring.
Innovation Solution
An artificial intelligence model-based abnormality diagnosis apparatus that receives image data from sensors, applies a pre-trained diagnosis model to identify abnormalities, corrects image data for alignment and environmental changes, and adjusts for equipment states to enhance diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video-based monitoring system with camera equipment is used, then real-time process management is enabled, but diagnostic accuracy is limited
Solution Approach 1:
The patent replaces the mechanical/optical video-based monitoring system with an AI-based diagnostic system that uses sensor data and machine learning models to detect and diagnose abnormalities, thereby improving diagnostic accuracy while maintaining real-time monitoring capability
Solution Approach 2:
The patent introduces AI models and sensor networks as intermediaries between the physical manufacturing process and the monitoring system, enabling more accurate diagnosis by processing data through intelligent algorithms rather than direct visual inspection
2Measurement precision
If operator visual inspection is used, then system complexity is reduced, but diagnostic accuracy and real-time management are compromised
Solution Approach 1:
The monitoring system performs self-diagnosis through AI models that automatically analyze sensor data and identify abnormalities without requiring operator intervention, thereby improving diagnostic accuracy while the system manages its own complexity through automation
Solution Approach 2:
The patent implements feedback loops where AI models continuously learn from diagnostic results and sensor data, improving accuracy over time while the automated feedback mechanism manages system complexity by reducing manual analysis requirements
3Measurement precision
If AI-based diagnosis model is implemented, then diagnostic accuracy is significantly improved, but device complexity increases
Solution Approach 1:
The patent pre-trains AI diagnosis models with extensive manufacturing data before deployment, so that when the system operates, the models are already optimized for accurate diagnosis, reducing the complexity of real-time decision-making processes
Solution Approach 2:
The patent divides the monitoring system into modular components including sensor networks, data processing modules, and AI model layers, allowing each component to be optimized independently while maintaining overall system accuracy and managing complexity through modular architecture
Data Source
AI summary
An abnormality diagnosis apparatus, located within a factory monitoring system, may include at least one processor; and memory having programmed thereon instructions that, when executed, are configured to cause the at least one processor to:receive image data about an inspection object from an image sensor; and diagnose an abnormality in the inspection object using the received image data and a pre-trained artificial intelligence-based diagnosis model.