Optimizing simulated-assisted verification of safety properties of cyber-physical systems
The validation of the safety properties of Cyber-Physical Systems (CPS) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety validation of CPS is Fault Injection (FI). Fault injection is a testing technique that aids in understanding how the system behavioral when stressed in an unusual way. The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost, as fault space is enormous and multidimensional. Therefore, traditional fault injection methods are not effective in terms of number of found faults and severity of them. In this thesis, we utilize simulation-based fault injection in the system models, which enables the test engineer to identify the fault in the early phase of system development. We first performed a systematic literature review to categorize the existing methods, fault models, metrics for system models. Then, we propose a fault injection method to inject faults into the MATLAB/Simulink model as white-box models using model transformation. We also worked on the fault injection in black-box models, which is based on Functional Mock-up Interface (FMI). Next, we investigated multiple methods to increase the efficiency (in terms of total number of critical faults and run time execution) of fault injection using sensitivity analysis, reinforcement learning (RL), and the Generative Adversarial Network (GAN). These methods utilize high-level domain knowledge of the model under test to set up the fault injection simulation. The proposed methods automatically configure faults in the model under test and find catastrophic faults that can violate the safety properties of the model in the early stage of system development. We compared the proposed method (RL-based and GAN-based) with random-based fault injection, and our proposed method outperformed random-based fault injection in terms of the severity or number of faults found. We also demonstrated our method in Hazard Analysis and Risk Assessment (HARA), specified in ISO 26262 (functional safety standard in automotive), identifies malfunctions that could lead to hazards, and rates their risks.
Antwerp : University of Antwerp, Faculty of Applied Engineering , 2024
xxiv, 164 p.
Supervisor: Denil, J. [Supervisor]
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The publisher created published version Available from 17.01.2025
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Creation 11.01.2024
Last edited 19.01.2024
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