No CrossRef data available.
Article contents
Extending the function failure modes taxonomy for intelligent systems with embedded AI components
Published online by Cambridge University Press: 16 May 2024
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Early consideration of failure modes in the feature development process is essential to identify and trace risks across the physical and embedded AI components of intelligent systems, to enhance the robustness of the feature delivery as well as trust in the AI. This paper introduces an extension of the AIAG/VDA function failure modes taxonomy, to facilitate the integrated analysis of complex intelligent systems with embedded AI. A case study of an autonomous driving feature is discussed as validation of the proposed taxonomy.
Keywords
- Type
- Artificial Intelligence and Data-Driven Design
- Information
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- The Author(s), 2024.
References
AIAG and VDA (2019), FMEA Handbook: design FMEA, process FMEA, supplemental FMEA for monitoring et system response, Southfild, MI.Google Scholar
Aslansefat, K, Kabir, S, Abdullatif, ARA et al. (2021) Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems. Computer. 54(8): 66-76. https://dx.doi.org/10.1109/MC.2021.3075054.CrossRefGoogle Scholar
Atif, M, Ceccarelli, A, Zoppi, T, Gharib, M and Bondavalli, A. (2022), "Robust Traffic Sign Recognition Against Camera Failures", In IEEE Jrnl Intelligent Transp Syst, 3:709-722, https://dx.doi.org/10.1109/OJITS.2022.3213183.CrossRefGoogle Scholar
Avizienis, A, Laprie, J C, Randell, B and Landwehr, C. (2004), "Basic concepts and taxonomy of dependable and secure computing", In: IEEE Trans Dep Secure Comp, 1:1:11-33, https://dx.doi.org/10.1109/TDSC.2004.2.CrossRefGoogle Scholar
Barwise, G. (1977) Studies in Logic and the Foundations of Mathematics, Elsevier, Volume 90, 1977, Pages 5-46, https://doi.org/10.1016/S0049-237X(08)71097-8.CrossRefGoogle Scholar
Blache, K M and Shrivastava, A B. (1994), "Defining failure of manufacturing machinery and equipment", Proceedings Annual Reliability and Maintainability Symposium; 69-75.Google Scholar
Blischke, W R, Murthy, D N P. (2000), Reliability Modeling, Prediction, and Optimization, John Wiley & Sons.CrossRefGoogle Scholar
BusinessInsider (2023) Report available from https://www.businessinsider.com/volkswagen-cruise-control-accelerates-owners-car-without-warning-report-2023-1?r=US&IR=T, accessed 28 October 2023.Google Scholar
Clausing, D and Frey, D (2004), Failure Modes And Two Types Of Robustness, INCOSE International Symposium, 14: 489-501. https://doi.org/10.1002/j.2334-5837.2004.tb00511.xCrossRefGoogle Scholar
Chu, T-L, Yuea, M, Postma, W. (2011), "A Summary of Taxonomies of Digital System Failure Modes", PSAM 2011 Helsinki. https://www.nrc.gov/docs/ML1206/ML120680552.pdf. Accessed: 15/09/2023.Google Scholar
Collins, J.A. (1993), Failure of Materials in Mechanical Design: Analysis, Prediction, Prevention, 2nd ed. Wiley Interscience; 1993.Google Scholar
Eifler, T., Campean, F., Husung, S., Schleich, B. (2023) ‘Perspectives on Robust Design – An Overview of Challenges and Research Areas Across Industry Fields’, in Proceedings of the International Conference on Engineering Design (ICED23), Bordeaux, France, 24-28 July 2023. https://dx.doi.org/10.1017/pds.2023.289CrossRefGoogle Scholar
Farag, W. (2019) Traffic signs classification by deep learning for advanced driving assistance systems,” Intell. Decis. Technol., vol. 13, no. 3, pp. 305-314, https://dx.doi.org/10.3233/IDT-180064.Google Scholar
Hirtz, J, Stone R, B, McAdams, D A et al. . (2002), "A functional basis for engineering design: Reconciling and evolving previous efforts", Res Eng Design 13, 65–82. https://doi.org/10.1007/s00163-001-0008-3CrossRefGoogle Scholar
Hu, T, Zhang, H, Zhou, J. (2023), "Machine learning-based model for recognizing the failure modes of FRP-strengthened RC beams in flexure", Case Studies in Construction Materials. Volume 18. doi.org/10.1016/j.cscm.2023.e02076.Google Scholar
BS-ISO 21448 (2022) Road Vehicles: Safety of the Intended Functionality, ISBN 978 0 539 23095 6.Google Scholar
Koopman, P and Wagner, M Challenges in Autonomous Vehicle Testing and Validation. SAE Int. J. Trans. Safety 4(1):15-24, 2016 https://doi.org/10.4271/2016-01-0128CrossRefGoogle Scholar
Korsunovs, A, Doikin, A, Campean, F, et al. . Towards a Model-Based Systems Engineering Approach for Robotic Manufacturing Process Modelling with Automatic FMEA Generation. Proceedings of the Design Society. 2022;2:1905-1914. https://dx.doi.org/10.1017/pds.2022.193CrossRefGoogle Scholar
Kumar, R S S, O`Brien, D, Albert, K, Viljoen, S and Snover, J. (2019), Failure Modes in Machine Learning. [online] Microsoft. https://arxiv.org/abs/1911.11034 (accessed: 13/10/2023)Google Scholar
McNelles, P, Zeng, Z C, Renganathan, G, Chirila, M, Lixuan Lu, L. (2017), "Failure mode taxonomy for assessing the reliability of Field Programmable Gate Array based Instrumentation and Control systems", Annals of Nuclear Energy, Volume 108, Pages 198-228. https://doi.org/10.1016/j.anucene.2017.04.033.Google Scholar
O'Halloran, B M, Stone, R B and Tumer, I Y. (2012), "A failure modes and mechanisms naming taxonomy", Proceedings Annual Reliability and Maintainability Symposium, pp. 1-6, 2012.Google Scholar
Scott, P, and Yampolskiy, R. (2020), "Classification Schemas for Artificial Intelligence Failures", Delphi - Interdisciplinary Review of Emerging Technologies. Volume 2, Issue 4. pp. 186-199 https://doi.org/10.21552/delphi/2019/4/8CrossRefGoogle Scholar
Shafique, M et al. . (2020), “Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead”. IEEE Design and Test, 37(2), pp. 30-57, https://doi.org/10.1109/MDAT.2020.2971217.CrossRefGoogle Scholar
Stone, R B, and Wood, K L. (2000), "Development of a Functional Basis for Design", ASME. J. Mech. Des. 122(4): 359–370. https://doi.org/10.1115/1.1289637CrossRefGoogle Scholar
Taguchi, G. (1986), Introduction to quality engineering – designing quality into products and processes, Asian Productivity Association. ISBN: 978-9283310846.Google Scholar
Thieme C, A, Mosleh, A, Utne I, B, Hegde, J. (2020), "Incorporating software failure in risk analysis—part 1: software functional failure mode classification", Reliability Engineering & System Safety, Volume 197. https://doi.org/10.1016/j.ress.2020.106803.Google Scholar
Tumer, I Y, Stone, R B and Bell, D G. (2003), "Requirements for a Failure Mode Taxonomy for Use in Conceptual Design", In International Conference on Engineering Design, Stockholm Sweden.Google Scholar
Vermaas, PE. (2009). The flexible meaning of function in engineering. In Leifer, L., Skogstad, P., Norell Bergendahl, M., & Grimheden, M. (Eds.), Proc ICED 09, pp. 2.113-2.124. The Design Society.Google Scholar
Yildirim, U., Campean, F., and Williams, H. (2017) Function modeling using the system state flow diagram. AI-EDAM, 31:4:413-435. https://doi.org/10.1017/S0890060417000294Google Scholar
Yildirim, U, Campean, F, Korsunovs, A, Doikin, A. Flow heuristics for functional modelling in model-based systems engineering. Proceedings of the Design Society. 2023;3:1895-1904. https://dx.doi.org/10.1017/pds.2023.190CrossRefGoogle Scholar
You have
Access
Open access