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

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

Reliability Engineering and System Safety, 2024, vol. 250, issue C

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.

Keywords: Artificial intelligence; Reliability assessment; Risk assessment; dFMECA; dFMEA; Partial information (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003806

DOI: 10.1016/j.ress.2024.110308

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