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AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications

Author

Listed:
  • Grabill, Nicholas
  • Wang, Stephanie
  • Olayinka, Hammed A.
  • De Alwis, Tharindu P.
  • Khalil, Yehia F.
  • Zou, Jian

Abstract

Design failure modes, effects, and criticality analysis (d-FMECA)22The term FMEA and FMECA are used interchangeably throughout this paper. The user has the option in the interface to choose either FMEA or FMECA based on their desired application. is a bottom-up, semi-quantitative risk assessment approach that is used by reliability engineers across all industries (nuclear, chemical, environmental, pharmaceuticals, aerospace, etc.) for identifying the effects of postulated components failure modes such as solenoid-operated valves (SOV), motor-operated valves (MOV), controllers, pumps, sensors of various types, printed circuit boards (PCBs). This research aims to develop a novel AI-augmented tool that guides, in real-time, the risk-analyst to a host of potential failure modes and their effects for each component contained in a bigger system. Through a user-friendly graphical interface and a robust statistical modeling backend, the AI-driven tool streamlines the risk assessment process by prompting the risk analyst to input a system’s name and subsequently generate an extensive array of failure modes and associated effects for each constituent component within the system. This AI-augmented tool allows the user to select either a simplified d-FMEA or a detailed d-FMECA for the system under investigation. This novel AI-driven tool offers significant effort and time savings in conducting d-FMECA, which is known to be a labor-intensive engineering task. In addition, this tool can be used for training risk and reliability professionals.

Suggested Citation

  • Grabill, Nicholas & Wang, Stephanie & Olayinka, Hammed A. & De Alwis, Tharindu P. & Khalil, Yehia F. & Zou, Jian, 2024. "AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003806
    DOI: 10.1016/j.ress.2024.110308
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    References listed on IDEAS

    as
    1. Liu, Peide & Li, Ying, 2021. "An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Kapusuzoglu, Berkcan & Mahadevan, Sankaran, 2021. "Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    5. Wu, Hao & Xu, Yanwen & Liu, Zheng & Li, Yumeng & Wang, Pingfeng, 2023. "Adaptive machine learning with physics-based simulations for mean time to failure prediction of engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    6. Zhang, Pei & Zhang, Zhen-Ji & Gong, Da-Qing, 2024. "An improved failure mode and effect analysis method for group decision-making in utility tunnels construction project risk evaluation," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Barlow, Richard E. & Proschan, Frank, 1975. "Importance of system components and fault tree events," Stochastic Processes and their Applications, Elsevier, vol. 3(2), pages 153-173, April.
    8. Li, Ying & Liu, Peide & Li, Gang, 2023. "An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    10. Dhalmahapatra, Krantiraditya & Garg, Ashish & Singh, Kritika & Xavier, Nirmal Francis & Maiti, J., 2022. "An integrated RFUCOM – RTOPSIS approach for failure modes and effects analysis: A case of manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    11. Wang, Qun & Jia, Guozhu & Jia, Yuning & Song, Wenyan, 2021. "A new approach for risk assessment of failure modes considering risk interaction and propagation effects," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Ziegler Haselein, Bruno & da Silva, Jonny Carlos & Hooey, Becky L., 2024. "Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
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