Digital Twin Fault Simulation for Automated Installation Root Causes
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Solution Overview
Problem
Automated installations face challenges in identifying and understanding the causes of faults due to temporal and local fault propagation, which complicates the identification and rectification of unforeseen installation states, as existing methods like Failure Mode and Effect Analysis (FMEA) and Root Cause Analysis require excessive effort and may not cover all possible fault scenarios.
Innovation Solution
A method that continuously captures real installation states, determines the most recent fault-free state, initializes a digital twin, simulates the installation state with assumed faulty elements, and compares the simulated state with the real state to identify the faulty element, allowing for iterative refinement until a match is found, thereby eliminating the need for extensive modeling and simulating all possible fault states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive fault analysis is performed by simulating all possible fault states, then fault identification completeness is improved, but computational complexity and time consumption increase excessively
Solution Approach 1:
Instead of simulating all possible fault states forward from normal operation, the invention inverts the approach by starting from the observed fault state and working backward to identify the cause. The digital twin is initialized with the fault state and simulated backward in time to find the root cause, avoiding the need to enumerate all possible fault scenarios.
Solution Approach 2:
A digital twin (virtual copy) of the automated installation is created to perform fault analysis simulations. This copy allows comprehensive fault state exploration without affecting the real system and enables repeated simulations with different initial conditions to identify fault causes efficiently.
2Reliability
If traditional fault analysis methods like FMEA are used, then systematic fault coverage is improved, but analysis time and effort increase excessively
Solution Approach 1:
The invention replaces manual, systematic fault analysis methods (mechanical process of FMEA) with automated digital simulation. The digital twin automatically performs fault state simulations and comparisons, substituting the time-consuming manual analysis process with computational automation that provides comprehensive coverage without proportional time increase.
Solution Approach 2:
The method uses feedback by continuously comparing the simulated fault states with the actual observed fault state. This comparison provides feedback that guides the identification process, allowing the system to converge on the correct fault cause efficiently rather than exhaustively checking all possibilities.
3Measurement precision
If digital twin simulation is used to model all fault combinations, then fault analysis accuracy is improved, but modeling effort and resource requirements increase excessively
Solution Approach 1:
Instead of modeling all possible fault combinations in the digital twin, the invention applies partial action by only simulating the specific fault scenario that matches the observed symptoms. The backward simulation from the fault state focuses computational resources on the relevant fault path rather than exhaustively modeling all possible faults, reducing modeling effort while maintaining accuracy.
Data Source
AI summary
A method, device for identifying causes of faults in automated systems and an automated system which forms the device for identifying causes of faults in automated systems, wherein within a digital twin of the automated system, at least one element of the digital twin is assumed to be faulty and then simulated using the digital twin until a fault time, and where at least one faulty element of the automated system is identified as the cause of a fault based on the at least one element assumed to be faulty.