Center for Industrial Mathematics and Statistics » REU 2023 – AI-augmented Reliability Predictions using Failure Modes, Effects, and Criticality Analysis for Industrial Applications

Students: Nicholas Grabill and Stephanie Wang

Overview

Industrial Sponsor: Collins Aerospace

Industrial Liaison: John Khalil

Faculty Advisors: Dr. Jian Zou and Dr. Tharindu De Alwis

Design failure modes and effects analysis (d-FMEA) is a risk assessment tool that is used by reliability engineers for identifying the effects of postulated failure modes of components such as solenoid valves, motor-operated valves, controllers, pump, sensors, printed circuit boards (PCBs). We have developed a novel AI-based tool that guides the user to a host of 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 users 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. By automating this aspect of d-FMEA, the AI-based solution offers significant time savings for reliability engineers, thereby greatly enhancing the risk mitigation process and enabling more efficient and targeted risk mitigation strategies.