Digital Twin Machine Performance Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine performance identification and issue resolution methods are inefficient in detecting vulnerabilities and deploying workarounds in real-time, leading to suboptimal functioning or breakdowns, which disrupt shop floor operations.
Innovation Solution
A computer-implemented method using a digital twin of a machine to run simulations, identify performance issues, and deploy capabilities such as backup machines or accessories to address these issues, ensuring continuous operation and maintaining safety and quality standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional machine performance identification methods are used, then the system structure remains simple, but the detection speed and real-time response capability are insufficient
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical machine to simulate and predict performance issues. The digital twin replicates the machine's components, operations, and environmental conditions, allowing virtual testing without interfering with the actual machine. This enables rapid detection and analysis of potential failures through simulation rather than requiring complex real-time monitoring of the physical system alone.
Solution Approach 2:
The system performs preliminary simulations on the digital twin before actual machine failures occur. By running virtual tests and comparing results against historical data and machine specifications, the system identifies potential performance issues in advance, allowing preventive maintenance before the physical machine experiences problems.
2Reliability
If real-time performance monitoring is implemented, then the reliability of machine operation improves, but the complexity of the monitoring system increases
Solution Approach 1:
Instead of directly monitoring the complex physical machine in real-time, the system creates a simplified digital representation (digital twin) that mirrors the machine's state. This virtual copy can be monitored and simulated more easily than the physical machine, reducing monitoring system complexity while maintaining reliability through continuous virtual observation of machine conditions.
Solution Approach 2:
The digital twin acts as an intermediary between the physical machine and the monitoring system. Rather than directly analyzing complex physical machine data, the system monitors the digital twin's simulated state, which translates physical conditions into easier-to-process virtual representations, simplifying the monitoring architecture.
3Productivity
If traditional issue resolution methods are used, then the implementation process is simple, but the productivity loss and operational disruption increase
Solution Approach 1:
The system pre-identifies potential performance issues through simulation and prepares workaround strategies in advance. When actual machine problems occur, the system can quickly deploy predetermined solutions or alternative operations based on the digital twin's analysis, minimizing operational disruption and maintaining productivity without requiring complex real-time decision-making.
Solution Approach 2:
The digital twin continuously receives feedback from the physical machine's actual performance data and adjusts simulations accordingly. This feedback loop enables the system to learn from real-world conditions and refine its predictions, improving the accuracy of performance identification and the effectiveness of deployed workarounds over time.
Data Source
AI summary
A method includes identifying, by a computing device, activities of a machine using a digital twin of a machine; running, by the computing device, simulations of the activities on the digital twin; determining, by the computing device, a performance issue in the machine using the simulations; and deploying, by the computing device, a capability to address the performance issue.