Machine Critical Zone Mapping Using Digital Twin Incident Propagation
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Solution Overview
Problem
Industrial machine environments pose challenges in identifying critical zones where worker safety is at risk due to the complexity of machine operations and the propagation of potential incidents, which existing safety measures fail to adequately address.
Innovation Solution
A computer-implemented method using digital twin simulations and historical incident records to identify critical zones by analyzing machine operating contexts, aggregate energy, and potential impact areas, providing intensity levels and safety equipment recommendations to workers through augmented reality devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twin simulations and historical incident record analysis are implemented to identify critical zones, then worker safety is improved, but device complexity increases
Solution Approach 1:
The system performs digital twin simulations and analyzes historical incident records in advance to identify critical zones before actual incidents occur. This preliminary action allows the system to proactively determine areas surrounding machines that will be impacted by potential incidents, enabling preventive safety measures rather than reactive responses.
Solution Approach 2:
The patent creates a digital twin - a virtual copy of the physical machine environment - to simulate incident propagation without requiring physical experimentation. This copying approach allows safe analysis of hazardous scenarios and enables the identification of critical zones through virtual modeling rather than direct physical testing.
2Measurement precision
If comprehensive digital twin simulations are performed to determine aggregate energy and affected areas, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system pre-calculates incident propagation paths, aggregate energy values, and affected areas through digital twin simulations during system setup or during periods when production is not affected. By performing these computationally intensive measurements in advance, the system achieves high measurement precision without causing time loss during critical operational periods.
Solution Approach 2:
The system dynamically adjusts the level of simulation detail and computational resources based on the specific machine, incident type, and operational context. For routine assessments, simplified models are used to reduce processing time, while for critical or unusual scenarios, more comprehensive simulations are performed to ensure measurement precision.
Data Source
AI summary
Identifying critical zones of machines is provided. A digital twin simulation of a machine is performed. An analysis of a set of historical incident records corresponding to the machine is performed to determine areas surrounding the machine that will be impacted by propagation of different types of incidents corresponding to the machine. A task performed by the machine, an operating context of the machine, an aggregate energy of the machine, and an area in the industrial machine environment affected by propagation of released energy from the machine are identified based on the digital twin simulation and the analysis of the set of historical incident records. A set of critical zones corresponding to the machine is identified based on the task performed by the machine, the operating context of the machine, the aggregate energy of the machine, and the area in the industrial machine environment affected by propagation of released energy.