Application of statistical data and methods to establish RPN ratings of FMEA method for construction projects
Abstract
The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.
Keyword : failure mode and effects analysis, risk priority number, construction engineering, total loss model, failure rate
How to Cite
Juan, Y.-K., Sheu, U.-Y., & Chen, K.-S. (2023). Application of statistical data and methods to establish RPN ratings of FMEA method for construction projects. Journal of Civil Engineering and Management, 29(7), 662–668. https://doi.org/10.3846/jcem.2023.19942
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Chakhrit, A., & Chennoufi, M. (2021). Fuzzy multi-criteria approach for criticality assessment and optimization of decision making. Journal of Intelligent and Fuzzy Systems, 41(2), 2701–2716. https://doi.org/10.3233/JIFS-202362
Canbolat, P. G. (2020). Bounded rationality in clearing service systems. European Journal of Operational Research, 282(2), 614–626. https://doi.org/10.1016/j.ejor.2019.10.013
Chen, K. S., Wang, C. C., Wang, C. H., & Huang, C. F. (2010). Application of RPN analysis to parameter optimization of passive components. Microelectronics Reliability, 50(12), 2012–2019. https://doi.org/10.1016/j.microrel.2010.06.014
Chen, K. S., & Yang, C. M. (2018). Developing a performance index with a Poisson process and an exponential distribution for operations management and continuous improvement. Journal of Computational and Applied Mathematics, 343, 737–747. https://doi.org/10.1016/j.cam.2018.03.034
Chen, K. S., & Yu, C. M. (2022). Lifetime performance evaluation and analysis model of passive component capacitor products. Annals of Operations Research, 311(1), 51–64. https://doi.org/10.1007/s10479-021-04242-6
Chen, J., Chi, H. L., Du, Q., & Wu, P. (2022). Investigation of operational concerns of construction crane operators: An approach integrating factor clustering and prioritization. Journal of Management in Engineering, 38(4), 04022020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001044
Gumasing, M. J. J., Prasetyo, Y. T., Ong, A. K. S., Carcellar, M. R. I. M., Aliado, J. B. J., Nadlifatin, R., & Persada, S. F. (2022). Ergonomic design of apron bus with consideration for passengers with mobility constraints. Safety, 8(2), 33. https://doi.org/10.3390/safety8020033
Gong, J., Luo, Y., Qiu, Z., & Wang, X. (2022). Determination of key components in automobile braking systems based on ABC classification and FMECA. Journal of Traffic and Transportation Engineering (English Edition), 9(1), 69–77. https://doi.org/10.1016/j.jtte.2019.01.008
Johnson, K. G., & Khan, M. K. (2003). A study into the use of the Process Failure Mode and Effects Analysis (PFMEA) in the automotive industry in the UK. Journal of Materials Processing Technology, 139(1–3), 348–356. https://doi.org/10.1016/S0924-0136(03)00542-9
Jiang, W., Zhang, Z., & Deng, X. (2019). A novel failure mode and effects analysis method based on fuzzy evidential reasoning rules. IEEE Access, 7, 113605–113615. https://doi.org/10.1109/ACCESS.2019.2934495
Kushwaha, D. K., Panchal, D., & Sachdeva, A. (2022). Intuitionistic fuzzy modeling-based integrated framework for performance analysis of juice clarification unit. Applied Soft Computing, 124, 109056. https://doi.org/10.1016/j.asoc.2022.109056
Li, M., Chen, K. S., Yu, C. M., & Yang, C. M. (2021). A fuzzy evaluation decision model for the ratio operating performance index. Mathematics, 9(3), 262. https://doi.org/10.3390/math9030262
Liu, Y., & Tang, Y. (2022). Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure. Scientific Reports, 12(1), 6812. https://doi.org/10.1038/s41598-022-10828-2
Mattsson, F. (1995). An introduction to risk analysis for medical devices. Compliance Engineering, 11(12), 47–57.
Ng, Y. J., Yeo, M. S. K., Ng, Q. B., Budig, M., Muthugala, M. A. V. J., Samarakoon, S. M. B. P., & Mohan, R. E. (2022). Application of an adapted FMEA framework for robot-inclusivity of built environments. Scientific Reports, 12(1), 3408. https://doi.org/10.1038/s41598-022-06902-4
Ouyang, L., Che, Y., Yan, L., & Park, C. (2022). Multiple perspectives on analyzing risk factors in FMEA. Computers in Industry, 141, 103712. https://doi.org/10.1016/j.compind.2022.103712
Rakesh, R., Jos, R. C., & Mathew, G. (2013). FMEA analysis for reducing breakdowns of a sub system in the life care product manufacturing industry. International Journal of Engineering Science and Innovative Technology, 2(2), 218–225.
Von Ahsen, A., Petruschke, L., & Frick, N. (2022). Sustainability failure mode and effects analysis – A systematic literature review. Journal of Cleaner Production, 363, 132413. https://doi.org/10.1016/j.jclepro.2022.132413
Tang, Y., Tan, S., & Zhou, D. (2023). An improved failure mode and effects analysis method using belief Jensen–Shannon divergence and entropy measure in the evidence theory. Arabian Journal for Science and Engineering, 48(5), 7163–7176. https://doi.org/10.1007/s13369-022-07560-4
Wang, W., Liu, X., Qin, Y., & Fu, Y. (2018). A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Safety Science, 110, 152–163. https://doi.org/10.1016/j.ssci.2018.08.009
Xie, S., Chen, Y., Dong, S., & Zhang, G. (2020). Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence. Journal of Loss Prevention in the Process Industries, 67, 104214. https://doi.org/10.1016/j.jlp.2020.104214
Xie, J. F., Wan, H., Zhang, S. Z., Han, X. T., Shi, J. T., & Li, L. (2022). Reliability analysis of a pulsed high magnetic field facility at WHMFC. IEEE Transactions on Applied Superconductivity, 32(6), 4300905. https://doi.org/10.1109/TASC.2022.3157803
Yuan, Y., & Tang, Y. (2022). Fusion of expert uncertain assessment in FMEA based on the negation of basic probability assignment and evidence distance. Scientific Reports, 12(1), 8464. https://doi.org/10.1038/s41598-022-12360-9