Conveyor Digital Twin Configuration for Faster Testing and Diagnostics
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Solution Overview
Problem
Existing manufacturing and automation systems face challenges in efficiently configuring and diagnosing conveyor systems due to complex requirements and the need for manual configuration, which is time-consuming and lacks the necessary specificity and granularity, leading to potential operational issues and downtime.
Innovation Solution
A system and method for conveyor configuration and testing that utilizes input data to simulate conveyor operations, monitors operational parameters like power usage and temperature, and adjusts configuration parameters using machine learning models to ensure optimal performance, allowing for automatic adjustments and continuous simulation until termination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration methods are used for conveyor systems, then configuration can be performed with simple tools, but the configuration process takes a substantial amount of time and lacks specificity
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical conveyor system that can be configured, simulated, and diagnosed without affecting the actual system. This digital model allows rapid configuration testing and parameter optimization before implementing changes on the physical system, dramatically reducing configuration time while maintaining simplicity through software-based manipulation of the digital replica.
Solution Approach 2:
The system performs preliminary configuration testing and validation in the digital twin environment before deploying to the actual conveyor system. By pre-configuring and pre-testing parameters, configurations, and scenarios in the virtual model, the patent eliminates time-consuming trial-and-error adjustments on the physical system, thereby reducing overall configuration time while keeping the process manageable.
2Reliability
If comprehensive monitoring of operational parameters is implemented, then system performance and reliability are improved, but system complexity increases
Solution Approach 1:
The digital twin serves as an intermediary between the physical conveyor system and the monitoring/analysis functions. It receives data from sensors on the actual system, processes and simulates operational parameters in the virtual model, and provides diagnostic insights without requiring complex direct monitoring infrastructure on the physical system. This intermediary approach enhances reliability through comprehensive analysis while managing complexity by centralizing monitoring functions in the software domain.
Data Source
AI summary
A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.