Filling Process Anomaly Detection With Real-Time Video Analysis
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Solution Overview
Problem
Conventional filling systems fail to detect anomalies in real-time during the filling process, leading to wasted containers due to undetected splashes, drips, or unsafe filling component-fluid distances, and lack automated tuning capabilities to correct these issues.
Innovation Solution
A computerized method using video analysis and machine learning to detect anomalies such as filling component submersion, splash, drip, and unsafe filling component-fluid distance, generating alerts and automatically adjusting filling machine parameters to prevent these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time video analysis is implemented to detect anomalies during filling, then detection capability and product quality are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent replaces manual quality control inspection with automated video analysis and machine learning algorithms. The system captures video of the filling process and uses computer vision to automatically detect anomalies such as splashes, drips, and unsafe distances, eliminating the need for human reviewers and significantly improving detection precision while maintaining manageable system complexity through automation.
Solution Approach 2:
The patent introduces an intermediary alerting system that bridges the gap between anomaly detection and machine response. When anomalies are detected via video analysis, the system generates alerts that can trigger automated responses, serving as a mediator between the detection mechanism and the filling machine control, thereby improving detection capability without requiring direct complex integration between all system components.
2Productivity
If automated anomaly detection and machine tuning are implemented, then productivity and waste reduction are improved, but device complexity increases
Solution Approach 1:
The patent implements a feedback loop where the video analysis system continuously monitors the filling process, detects anomalies in real-time, and triggers alerts that can automatically adjust machine parameters or stop the filling process. This feedback mechanism enables automated tuning of the filling machine based on detected conditions, improving productivity by reducing waste from defective fills while managing complexity through systematic control loops.
Solution Approach 2:
The system enables the filling machine to self-adjust based on automated anomaly detection. When the video analysis system identifies issues such as splashes or unsafe distances, the machine can automatically modify its operation parameters without external intervention, improving productivity by continuously optimizing the filling process while reducing the need for complex external control systems.
3Measurement precision
If continuous monitoring of filling process is performed, then anomaly detection accuracy is improved, but energy consumption and processing load increase
Solution Approach 1:
The patent employs periodic video capture and analysis at critical moments during the filling process rather than continuous uninterrupted analysis. The system monitors the filling process at key stages where anomalies are most likely to occur, maintaining high detection accuracy while reducing the overall processing load and energy consumption compared to constant full-process analysis.
Data Source
AI summary
The techniques described herein relate to computerized methods and apparatuses for detecting anomalies within a container using one or more images of a video of a filling process in which the container is filled with liquid. The techniques described herein further relate to computerized methods and apparatuses for detecting the anomalies in real-time while the container is filled with fluid, and upon detecting at least one anomaly, outputting an alert, or altering one or more parameters of the filling process the filling machine is configured to perform.