Enabling Model-Based RAMS Through LLM-Driven Legacy Data Transformation
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Abstract
The rapid digital transformation in engineering, coupled with the development of increasingly complex systems, is pushing industries to develop smarter and more efficient methods for system development. Major stakeholders/ industries are moving towards a model-based framework for systems engineering, RAM, and safety analysis to manage growing system complexity while maintaining data consistency and traceability.
The convergence and consolidation of previously document-based engineering approaches allows for the standardization and streamlined capture of knowledge across engineering disciplines. In this framework, data availability and interoperability can easily become a bottleneck without comparable innovation to tooling and processes. In more recent times Artificial Intelligence (AI) has been identified as a powerful enabler on this front. AI can assist engineers in developing RAMS models more efficiently by leveraging legacy data, such as historical FMECAs, and aligning it with standardized taxonomies to automatically and rapidly develop system models for downstream analysis requirements.
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Large Language Models, LLM, DRT, Digital Risk Twin, AI, RAMS, model-based FMEA, FMEA
Decardi-Nelson, B., Alshehri, A. S., Ajagekar, A., & You, F. (2024). Generative AI and process systems engineering: The next frontier. ArXiv
Grabill, N., Wang, S., Olayinka, H.A., De Alwis, T.P., Khalil, Y.F. and Zou, J., 2024. AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications. Reliability Engineering & System Safety, 250, p.110308.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V. and Zhou, D., 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, pp.24824-24837
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