Problem
Research questions and friction points this paper is trying to address.
representative dataset
failure analysis
real-time validation
automotive software systems
fault data
Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems
🤖 AI Summary
This work addresses the critical limitation in AI-driven automotive software fault analysis—the scarcity of representative fault datasets—by proposing a novel framework that integrates hardware-in-the-loop (HIL) simulation with real-time fault injection. For the first time, this approach enables synchronized generation of multimodal fault data, encompassing both time-series signals and textual logs, under both single-point and concurrent fault conditions. The framework effectively captures complex fault scenarios, yielding a comprehensive dataset that supports the training and validation of machine learning models. Empirical validation in a real-world testing environment demonstrates the framework’s practical applicability and effectiveness, offering a robust foundation for advancing fault diagnosis and resilience in automotive software systems.
📝 Abstract
Recently, thanks to its ability to extract knowledge from historical datasets, the data-driven approach has been widely used in various phases of the system development life cycle. During real-time system validation, remarkable achievements have been accomplished in developing an intelligent failure analysis based on historical data. However, despite its superiority over other conventional approaches, e.g., model-based and signal-based, the availability of representative datasets persists as a major challenge. Thus, for different engineering applications, new solutions to generate representative faulty data in different forms should be explored. Therefore, in this study, a novel approach based on Hardware-in-the-Loop (HIL) simulation and real-time Fault Injection (FI) method is proposed to generate and collect data samples under single and simultaneous faults for Machine Learning (ML) applications during system validation phases. The developed framework can generate not only sequential data, but also textual data including fault logs. The results show the applicability of the proposed framework in simulating and capturing the system behaviour under faults within the system components.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hardware-in-the-Loop (HIL)
Fault Injection (FI)
Representative Dataset Generation
AI-based Failure Analysis
Real-time Validation
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