An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage

Diao, Xiaoxu; Pietrykowski, Mike; Huang, Fuqun; Mutha, Chetan; Smidts, Carol

doi:10.1017/S0890060421000342

Title: An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage

Journal Article · 03 February 2022 · Artificial Intelligence for Engineering Design, Analysis and Manufacturing

DOI: https://doi.org/10.1017/S0890060421000342 · OSTI ID:1843513

; Pietrykowski, Mike; Huang, Fuqun; Mutha, Chetan; Smidts, Carol

Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

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Research Organization:: The Ohio State Univ., Columbus, OH (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI ID:: 1843513

Journal Information:: Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Journal Name: Artificial Intelligence for Engineering Design, Analysis and Manufacturing Vol. 36; ISSN 0890-0604

Publisher:: Cambridge University Press (CUP)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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