Digital Twin Maintenance Planning for Delayed Machine Downtime
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Companies face challenges in determining the optimal time for maintenance of machines and devices, which affects factory output and task management due to reduced capabilities and functionalities over time.
Innovation Solution
A method involving the creation of digital twins for physical ecosystems, simulating their performance, and generating task management plans to identify and address reductions in capacity and functionalities, allowing for staggered maintenance and maintaining productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance is performed intermittently to restore machine performance, then the machine can regain its functionalities, but the factory output and task management are affected due to unplanned downtime
Solution Approach 1:
The system performs preliminary actions by simulating machine performance degradation over time using digital twins before actual maintenance is needed. This allows planners to predict when maintenance will be required and schedule it in advance, avoiding unplanned downtime that disrupts factory output while ensuring machines are maintained before performance critically deteriorates
Solution Approach 2:
The system dynamically adjusts maintenance schedules by continuously simulating performance degradation and comparing it against task requirements. The digital twin models allow the system to optimize maintenance timing dynamically, balancing the need to restore machine performance with the need to maintain factory productivity by scheduling maintenance during periods of lower operational demand
2Productivity
If maintenance is delayed to maintain productivity, then factory output is preserved, but machine capability and capacity are reduced
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring simulated performance degradation through digital twins and using this information to determine optimal maintenance timing. The simulation provides feedback on how machine capability deteriorates over time, allowing planners to delay maintenance only as long as performance remains adequate for required tasks, thus maintaining productivity while preventing excessive capability loss
Solution Approach 2:
By simulating performance degradation in advance using digital twins, the system performs preliminary assessment of when machine capability will fall below acceptable thresholds. This allows maintenance to be scheduled just in time - delayed enough to maintain productivity but early enough to preserve necessary machine capability for upcoming tasks
3Productivity
If multiple digital twins are generated and simulated, then maintenance timing can be optimized, but computational complexity and data processing requirements increase
Solution Approach 1:
The system creates simplified digital copies (digital twins) of physical machines that replicate essential performance characteristics without requiring full physical duplication. These virtual models enable maintenance optimization through simulation while keeping system complexity manageable by focusing on critical performance parameters rather than modeling every physical detail
Solution Approach 2:
The system segments the maintenance planning process into distinct phases: creating individual digital twins for different machines, simulating their performance degradation separately, and then integrating results to optimize overall maintenance schedules. This segmentation allows complex multi-machine maintenance optimization to be broken down into manageable computational tasks
Data Source
AI summary
A method, computer system, and a computer program product for machine maintenance is provided. The present invention may include receiving data for one or more assets of a physical ecosystem. The present invention may include generating a digital twin of the physical ecosystem. The present invention may include simulating a performance of the digital twin of the physical ecosystem. The present invention may include generating a task management plan based on the performance of the digital twin.