Container Treatment Machine Digital Twin for Deviation Diagnosis
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Solution Overview
Problem
As container treatment machines become increasingly complex, manual monitoring for deviations and malfunctions becomes difficult, leading to late detection and incorrect identification of causes.
Innovation Solution
A container treatment machine equipped with at least two treatment units, sensors for determining state parameters, and a control unit that uses a digital twin and prediction modules (like neural networks) to detect and identify deviations and their causes, enabling continuous monitoring and timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If container treatment machines include more and more components to achieve specific treatment goals, then treatment capability and versatility are improved, but device complexity increases and manual monitoring becomes difficult
Solution Approach 1:
The patent creates a digital twin (digital copy) of the container treatment machine that replicates its structure, components, and operating parameters. This digital model allows for virtual monitoring, analysis, and optimization of the physical machine's state without adding physical monitoring components, thereby managing complexity while maintaining treatment capability.
Solution Approach 2:
The patent replaces manual mechanical monitoring with automated digital monitoring systems. Sensors collect data that is processed by the digital twin and prediction modules, substituting human operators' mechanical monitoring activities with automated computational analysis, thus reducing the burden of complexity.
2Reliability
If manual monitoring is used to detect deviations in complex machines, then operational costs are reduced, but detection reliability and timeliness deteriorate
Solution Approach 1:
The patent implements a feedback loop where sensors continuously monitor the machine's state parameters, the digital twin processes this data in real-time, and prediction modules generate alerts when deviations are detected. This automated feedback system ensures reliable and timely detection of malfunctions without manual intervention delays.
Solution Approach 2:
The digital twin system performs self-monitoring and self-diagnosis of the machine's state. The prediction modules automatically analyze sensor data and identify deviations, enabling the system to monitor itself without external human assistance, thus improving both reliability and response time.
3Measurement precision
If automated monitoring systems are implemented to detect deviations reliably, then detection accuracy is improved, but device complexity and initial costs increase
Solution Approach 1:
The digital twin serves multiple functions simultaneously: it stores machine data, processes sensor information, predicts deviations, generates maintenance alerts, and provides operational insights. This multi-functional approach consolidates what could be multiple separate complex systems into a single unified platform, improving measurement precision without proportionally increasing complexity.
Data Source
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AI summary
Container treatment machine (100) for treating containers, the container treatment machine comprising at least two treatment units (101, 102, 103) for treating containers, at least one sensor (143, 144) for determining a state parameter that is indicative of a state of the container after and/or during treatment and/or of an operating state of at least one treatment unit (101, 102, 103), and a control unit (180), wherein the control unit is configured to determine, based on the state parameter and a digital image of the container treatment machine as well as at least one operating parameter of the treatment units (101, 102, 103), whether there is a deviation in the operation of the container treatment machine and what the cause of the deviation is, and to control the container treatment machine (100) based on this.