Process Plant Simulation Testing With Random Fault Scenarios
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Solution Overview
Problem
Existing testing methods for process plant systems, such as Factory Acceptance Testing (FAT) and Site Acceptance Testing (SAT), fail to cover all scenarios, leading to low test coverage and potential operational issues remaining undetected.
Innovation Solution
A method involving a simulation component and a control component to simulate and control a process handling hardware, using at least partially randomly generated test instructions to test the process plant system, thereby uncovering weaknesses and failure conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods (FAT and SAT) are used, then the testing process is manageable and systematic, but test coverage is insufficient and potential operational issues remain undetected
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the process plant system that mirrors the physical system's structure, components, and behavior. This virtual model enables comprehensive testing scenarios including random fault injections and edge cases without risking the actual physical plant. The digital twin accepts test instructions and generates test results identical to what the physical system would produce, thereby achieving complete test coverage while maintaining manageable complexity through virtualization.
2Reliability
If comprehensive test scenarios are implemented to cover all possible situations, then operational issues can be detected, but the testing process becomes overly complex and difficult to manage
Solution Approach 1:
The digital twin autonomously executes comprehensive testing scenarios without requiring complex manual test design and management. The system automatically generates test instructions, simulates various operating conditions and fault scenarios, executes tests, and produces results. This self-service capability enables complete operational safety validation while keeping the testing procedure simple and manageable through automation.
Solution Approach 2:
The digital twin provides dynamic adaptability in testing by allowing random fault injections and variable test scenarios to be implemented easily. The virtual model can dynamically adjust test parameters, simulate different failure modes, and adapt test sequences based on system responses. This dynamic capability achieves comprehensive operational safety testing without the rigidity and complexity of predetermined exhaustive test procedures.
3Reliability
If random test instructions are generated to uncover hidden weaknesses, then test coverage improves, but predictability and control of the testing process decrease
Solution Approach 1:
The digital twin serves as an intermediary between the physical plant and the testing process. It receives and executes random test instructions, isolating the unpredictability of random testing from the physical system. The virtual model absorbs the chaos of random fault injections and returns structured test results, thereby improving vulnerability detection while maintaining testing process control through the mediating virtual environment.
Data Source
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AI summary
The disclosure relates to a method (100) for testing a process plant system (10), the process plant system (10) comprising a simulation component (30) and a control component (20), the simulation component (30) being configured to simulate process handling hardware (30') for a handling process of the process plant system (10), and the control component (20) being configured to control the handling process of the process handling hardware (30'), the method comprising: - obtaining test instruction data indicative of test instructions for testing the process plant system (10), wherein at least one of the test instructions is at least partially randomly generated; - testing the process plant system (10) by providing the test instruction data to the control component (20) for controlling the handling process of the simulation component (30); and - obtaining test result data indicative of a test result of the testing of the process plant system (10).