New Failure Mode and Effects Analysis based on D Numbers Downscaling Method

  • Baoyu Liu
  • Yong Hu
  • Yong Deng

Abstract

Failure mode and effects analysis (FMEA) is extensively applied to process potential faults in systems, designs, and products. Nevertheless, traditional FMEA, classical risk priority number (RPN), acquired by multiplying the ratings of occurrence, detection, and severity, risk assessment, is not effective to process the uncertainty in FMEA. Many methods have been proposed to solve the issue but deficiencies exist, such as huge computing quality and the mutual exclusivity of propositions. In fact, because of the subjectivity of experts, the boundary of two adjacent evaluation ratings is fuzzy so that the propositions are not mutually exclusive. To address the issues, in this paper, a new method to evaluate risk in FMEA based on D numbers and evidential downscaling method, named as D numbers downscaling method, is proposed. In the proposed method, D numbers based on the data are constructed to process uncertain information and aggregate the assessments of risk factors, for they permit propositions to be not exclusive mutually. Evidential downscaling method decreases the number of ratings from 10 to 3, and the frame of discernment from 2^{10} to 2^3 , which greatly reduce the computational complexity. Besides, a numerical example is illustrated to validate the high efficiency and feasibility of the proposed method.

Author Biographies

Baoyu Liu
College of Infomation Science and Technology, Jinan University, Guangzhou, China
Yong Hu
Big Data Decision Institute, Jinan University, Tianhe, Guangzhou, 510632, China
Yong Deng
Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China

References

[1] Abellán, J., Mantas, C. J., Castellano, J. G., (2017); A random forest approach using imprecise probabilities, Knowledge-Based Systems, 134, 72–84, 2017.
https://doi.org/10.1016/j.knosys.2017.07.019

[2] Abellán, J., (2017); Analyzing properties of deng entropy in the theory of evidence, Chaos Solitons & Fractals, 95, 195–199, 2017.
https://doi.org/10.1016/j.chaos.2016.12.024

[3] Bowles, J. B., Pelaez, C. E., (1995); Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis, Reliability Engineering System Safety, 50(2), 203–213, 1995.
https://doi.org/10.1016/0951-8320(95)00068-D

[4] Chin, K. S., Wang, Y. M., Gary, K. K. P., Yang, J. B., (2009); Failure mode and effects analysis using a group-based evidential reasoning approach, Computers & Operations Research, 36 (6), 1768–1779, 2009.
https://doi.org/10.1016/j.cor.2008.05.002

[5] Dempster, A. P., (1967). Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics, 38 (2), 325–339, 1967.
https://doi.org/10.1214/aoms/1177698950

[6] Deng, X., Han, D., Dezert, J., Deng, Y., Shyr, Y., (2016); Evidence Combination From an Evolutionary Game Theory Perspective, IEEE Transactions on Cybernetics, 46(9), 2070– 2082, 2016.
https://doi.org/10.1109/TCYB.2015.2462352

[7] Deng, X., Liu, Q., Deng, Y., (2016); Matrix games with payoffs of belief structures, Applied Mathematics and Computation, 273, 868–879, 2016.
https://doi.org/10.1016/j.amc.2015.10.056

[8] Deng, X., Liu, Q., Deng, Y., Mahadevan, S., (2016); An improved method to construct basic probability assignment based on the confusion matrix for classification problem, Information Sciences, 340, 250–261, 2016.

[9] Deng, Y., (2012). D numbers: theory and applications, Journal of Information and Computational Science, 9 (9), 2421–2428, 2012.

[10] Dong, Y., Wang, J., Chen, F., Hu, Y., Deng, Y., (2017); Location of facility based on simulated annealing and ZKW algorithms, Mathematical Problems in Engineering, Article ID 4628501, 2017.

[11] Du, Y., Lu, X., Su, X., Hu, Y., Deng, Y., (2016); New failure mode and effects analysis: An evidential downscaling method, Quality & Reliability Engineering, 32 (2), 737–746, 2016.
https://doi.org/10.1002/qre.1753

[12] Fei, L.; Wang, H.; Chen, L.; Deng, Y. (2017); A new vector valued similarity measure for intuitionistic fuzzy sets based on OWA operators, Iranian Journal of Fuzzy Systems, accepted, 2017.

[13] Goyal, R. K., Kaushal, S., (2016); A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization, Applied Intelligence, 1–10, 2016.

[14] Hu, Y., Du, F., Zhang, H. L., (2016); Investigation of unsteady aerodynamics effects in cycloidal rotor using RANS solver, Aeronatical Journal, 120(1228), 956–970, 2016.
https://doi.org/10.1017/aer.2016.38

[15] Jain, K., (2017). Use of failure mode effect analysis (fmea) to improve medication management process, International Journal of Health Care Quality Assurance, 30 (2), 175, 2017.
https://doi.org/10.1108/IJHCQA-09-2015-0113

[16] Jiang, W., Wang, S., (2017); An uncertainty measure for interval-valued evidences, International Journal of Computers Communications & Control, 12 (5), 631–644, 2017.
https://doi.org/10.15837/ijccc.2017.5.2950

[17] Jiang, W., Wang, S., Liu, X., Zheng, H., Wei, B., (2017); Evidence conflict measure based on OWA operator in open world, PloS one,12 (5), e0177828, 2017.
https://doi.org/10.1371/journal.pone.0177828

[18] Jiang, W., Xie, C., Zhuang, M., Tang, Y., (2017); Failure mode and effects analysis based on a novel fuzzy evidential method, Applied Soft Computing, 57, 672–683, 2017.
https://doi.org/10.1016/j.asoc.2017.04.008

[19] Kang, B., Chhipi-Shrestha, G., Deng, Y., Mori, J., Hewage, K., Sadiq, R. (2017); Development of a predictive model for clostridium difficile infection incidence in hospitals using gaussian mixture model and dempster-shafer theroy, Stochastic Environmental Research and Risk Assessment, accepted, 2017.

[20] Li, C., Mahadevan, S., (2016); An efficient modularized sample-based method to estimate the first-order sobol index, Reliability Engineering & System Safety, 153, 110–121, 2016.
https://doi.org/10.1016/j.ress.2016.04.012

[21] Li, C., Mahadevan, S., (2016); Relative contributions of aleatory and epistemic uncertainty sources in time series prediction, International Journal of Fatigue, 82, 474–486, 2016.
https://doi.org/10.1016/j.ijfatigue.2015.09.002

[22] Li, C.; Mahadevan, S. (2016); Role of calibration, validation, and relevance in multi-level uncertainty integration, Reliability Engineering & System Safety, 148, 32–43, 2016.
https://doi.org/10.1016/j.ress.2015.11.013

[23] Liu, H. C., You, J. X., Fan, X. J., Lin, Q. L., (2014); Failure mode and effects analysis using d numbers and grey relational projection method, Expert Systems with Applications, 41 (10), 4670–4679, 2014.
https://doi.org/10.1016/j.eswa.2014.01.031

[24] Liu, H. C., You, J. X., Li, P., Su, Q., (2016); Failure mode and effect analysis under uncertainty: An integrated multiple criteria decision making approach, IEEE Transactions on Reliability, 65 (3), 1380–1392, 2016.
https://doi.org/10.1109/TR.2016.2570567

[25] Liu, H. C., You, J. X., Li, P., Su, Q., (2016); Failure mode and effect analysis under uncertainty: An integrated multiple criteria decision making approach, IEEE Transactions on Reliability, 1–13, 2016.

[26] Liu, H.-C., You, J.-X., You, X.-Y., Shan, M.-M., (2015); A novel approach for failure mode and effects analysis using combination weighting and fuzzy vikor method, Applied Soft Computing, 28, 579–588, 2015.
https://doi.org/10.1016/j.asoc.2014.11.036

[27] Liu, J., Lian, F., Mallick, M., (2016); Distributed compressed sensing based joint detection and tracking for multistatic radar system, Information Sciences, 369, 100–118, 2016.
https://doi.org/10.1016/j.ins.2016.06.032

[28] Liu, T., Deng, Y., Chan, F., (2017); Evidential supplier selection based on DEMATEL and game theory, International Journal of Fuzzy Systems, DOI: 10.1007/s40815–017–0400–4, 2017.
https://doi.org/10.1007/s40815-017-0400-4

[29] Liu, W., Liu, H. B., Li, L. L., (2017); A multiple attribute group decision making method based on 2-d uncertain linguistic weighted heronian mean aggregation operator, International Journal of Computers Communications & Control, 12 (2), 254–264, 2017.
https://doi.org/10.15837/ijccc.2017.2.2792

[30] Liu, Z., Pan, Q., Dezert, J., Han, J.-W., He, Y., (2017); Classifier fusion with contextual reliability evaluation, IEEE Transactions on Cybernetics, 99, 1–14, 2017.

[31] Liu, Z., Pan, Q., Dezert, J., Martin, A., (2017); Combination of classifiers with optimal weight based on evidential reasoning, IEEE Transactions on Fuzzy Systems, 99, 1–15, 2017.

[32] Méndez-González, L. C., Ambrosio-Lazaro, R., Rodriguez-Borbon, I., Alvarado-Iniesta, A., (2016); Failure mode and effects analysis of power quality issues and their influence in the reliability of electronic products, Electrical Engineering, 1–13, 2016.

[33] Mo, H., Deng, Y., (2016); A new aggregating operator in linguistic decision making based on d numbers, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24 (6), 831–846, 2016.
https://doi.org/10.1142/S0218488516500379

[34] Mo, H., Gao, C., Deng, Y., (2015); Evidential method to identify influential nodes in complex networks, Journal of Systems Engineering and Electronics, 26 (2), 381–387, 2015.
https://doi.org/10.1109/JSEE.2015.00044

[35] Mo, H., Lu, X., Deng, Y., (2016); A generalized evidence distance, Journal of Systems Engineering and Electronics, 27 (2), 470–476, 2016.
https://doi.org/10.1109/JSEE.2016.00049

[36] Perdomo Ojeda, M., Salomon Llanes, J., (2016); Expanded failure mode and effects analysis: Advanced approach for reliability assessments, Revista Cubana de Ingeneria, 7 (2), 5–14, 2016.

[37] Pillay, A., Wang, J., (2003); Modified failure mode and effects analysis using approximate reasoning, Reliability Engineering System Safety, 79 (1), 69–85, 2003.
https://doi.org/10.1016/S0951-8320(02)00179-5

[38] Schneider, H., (1996); Failure mode and effect analysis: Fmea from theory to execution, Technometrics, 38 (1), 80–80, 1996.
https://doi.org/10.1080/00401706.1996.10484424

[39] Tooranloo, H. S., Ayatollah, A. S., (2016); A model for failure mode and effects analysis based on intuitionistic fuzzy approach, Applied Soft Computing, 49, 238–247, 2016.
https://doi.org/10.1016/j.asoc.2016.07.047

[40] Viejo, M. R., Sanchez-Izquierdo Riera, J., Molano, E., Barea Mendoza, J. A., Temprano, V. S., Diaz, C. L., Montejo Gonzalez, J. C., (2016); Improvement of the safety of a clinical process using failure mode and effects analysis: Prevention of venous thromboembolic disease in critical patients, Medicina Intensiva, 40 (8), 483, 2016.

[41] Wang, J., Hu, Y., Xiao, F., Deng, X., Deng, Y., (2016); A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis, Artificial intelligence in medicine, 69, 1–11, 2016.
https://doi.org/10.1016/j.artmed.2016.04.004

[42] Wang, J., Xiao, F., Deng, X., Fei, L., Deng, Y., (2016); Weighted Evidence Combination Based on Distance of Evidence and Entropy Function, International Journal of Distributed Sensor Networks, 12 (7), 2016.

[43] Wang, Y. M., Chin, K. S., Poon, G. K. K., Yang, J. B., (2009); Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean, Expert Systems with Applications, 36 (2), 1195–1207, 2009.
https://doi.org/10.1016/j.eswa.2007.11.028

[44] Xiao, F., Aritsugi, M., Wang, Q., Zhang, R., (2016); Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model, Artificial Intelligence in Medicine, 72 (C), 56–71, 2016.

[45] Xiao, F., Zhan, C., Lai, H., Tao, L., Qu, Z., (2017); New parallel processing strategies in complex event processing systems with data streams, International Journal of Distributed Sensor Networks, 13 (8), 1–15, 2017.

[46] Xu, S., Jiang, W., Deng, X., Shou, Y., (2017); A modified physarum-inspired model for the user equilibrium traffic assignment problem, Applied Mathematical Modelling, In Press, DOI: 10.1016/j.apm.2017.07.032, 2017.
https://doi.org/10.1016/j.apm.2017.07.032

[47] Yang, Z., Liu, P., Zhu, Y., Zhang, Y., (2016); A comprehensive reliability allocation method for series systems based on failure mode and effects analysis transformed functions, Proc. of the Institution of Mechanical Engineers, Part B- Journal of Engineering Manufacture, 230, 2239–2248, 2016.
https://doi.org/10.1177/0954405416673098

[48] Zhang, D., (2017); High-speed train control system big data analysis based on fuzzy rdf model and uncertain reasoning, International Journal of Computers Communications & Control, 12 (4), 577–591, 2017.
https://doi.org/10.15837/ijccc.2017.4.2914

[49] Zhang, Q., Li, M., Deng, Y., (2017); Measure the structure similarity of nodes in complex networks based on relative entropy, Physica A: Statistical Mechanics and its Applications, 10.1016/j.physa.2017.09.042, 2017.
https://doi.org/10.1016/j.physa.2017.09.042

[50] Zhang, X., Deng, Y., Chan, F. T. S., Adamatzky, A., Mahadevan, S., (2016); Supplier selection based on evidence theory and analytic network process, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230 (3), 562–573, 2016.
https://doi.org/10.1177/0954405414551105

[51] Zhang, X., Mahadevan, S., (2017); A game theoretic approach to network reliability assessment, IEEE Transactions on Reliability, 66 (3), 875–892, 2017.
https://doi.org/10.1109/TR.2017.2717186

[52] Zheng, H., Deng, Y., Hu, Y., (2017); Fuzzy evidential influence diagram and its evaluation algorithm, Knowledge-Based Systems, 131, 28–45, 2017.
https://doi.org/10.1016/j.knosys.2017.05.024

[53] Zheng, X., Deng, Y., (2017); Dependence assessment in human reliability analysis based on evidence credibility decay model and iowa operator, Annals of Nuclear Energy, accepted, 2017.

[54] Zhou, X., Deng, X., Deng, Y., Mahadevan, S., (2017); Dependence assessment in human reliability analysis based on d numbers and ahp, Nuclear Engineering and Design, 313, 243–252, 2017.
https://doi.org/10.1016/j.nucengdes.2016.12.001

[55] Zhou, X., Shi, Y., Deng, X., Deng, Y., (2017); D-DEMATEL: A new method to identify critical success factors in emergency management, Safety Science, 91, 93–104, 2017.
https://doi.org/10.1016/j.ssci.2016.06.014
Published
2018-04-13
How to Cite
LIU, Baoyu; HU, Yong; DENG, Yong. New Failure Mode and Effects Analysis based on D Numbers Downscaling Method. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 2, p. 205-220, apr. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2990>. Date accessed: 09 july 2020. doi: https://doi.org/10.15837/ijccc.2018.2.2990.

Keywords

Failure mode and effects analysis; Dempster-Shafer evidence theory; basic belief assignment; belief function; risk priority number; D numbers; evidential downscaling; aggregate assessment