An Area-Based Metrics to Evaluate Risk in Failure Mode and Effects Analysis Under Uncertainties

Abstract

Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA results.INDEX TERMS failure mode and effects analysis, D-S evidence theory, area-based metrics; epistemic uncertainty metric, risk priority number.

Application of Sustainable Education Innovation in the Integrated Teaching of Theory and Practice Adopted in the Auto Chassis Course

“…Australia's competence-based education and training (CBET) is a state-supervised vocational education model under the market's regulation. All along a nice bit of focus has been cast on exploiting effective teaching tactics in Australia [3,11]. In recent decades, quite a few local college scholars have attempted to carry out their research into integrated teaching strategies, primarily including thematic teaching method, interactive teaching mode, and roleplaying teaching, all of which fully pertain to the integrated model associating the theory with practice.…”

“…Despite its superiority in uncomplicated principles and strong operability, there are high possibilities of occurring errors caused by robust personal subjectivity in the rating process of the risk factors such as occurrence (O), severity (S), and detection (D). In view of the defects that related theoretical support could be insufficient in the attempt of equivalently quantifying risks by multiplying O, S, and D to obtain RPN and that risks represented by the same PRN could be indistinguishable, a couple of scholars such as Pillay and Wang [11] and Yan et al [3] suggested FMEA, which is based on evidence reasoning, and meanwhile simulated the diversity and uncertainty arising from the FMEA evaluating process for rating. is method enables experts in concerned area to evaluate risk factors with the conventional mode of digital rating.…”

“…Besides, equal attention needs to be paid in the cultivating process to both operational and mental skills. In this case, students majoring in auto chassis maintenance are required to integrate theory and technology into practical maintenance operations as soon as possible and shorten the breaking-in period in line with their industry as best as possible [3,4].…”