Copula-based decision support system for quality ranking in the manufacturing of electronically commutated motors
Quality ranking of finished products plays an important role in manufacturing systems. In this paper, we address the problem of quality ranking of electronically commutated (EC) motors by subjecting each finished product to a short measurement session. Based on the features calculated from these measurements, the motor quality is assessed by introducing a novel copula-based decision support system (DSS). The proposed DSS provides a full ranking of EC motors by integrating expert’s preferences and company’s quality standards. This approach overcomes the shortcomings of the traditional regression models, such as partial ranking and inconsistent evaluations with the expert’s expectations. We demonstrate the effectiveness of the proposed DSS on a test batch of 840 EC motors.
KeywordsDecision support system Copula-based regression EC motors Quality assessment
The research of the first author was supported by Ad Futura Programme of the Slovene Human Resources and Scholarship Fund. We also like to acknowledge the support of the Slovenian Research Agency through Research Programmes J2-2353, P2-0001 and L2-4160. The work was partly done in the frame of the Competence Centre for Advance Control Technologies. Operation is partly financed by the Republic of Slovenia, Ministry of Higher Education, Science and Technology and European Union (EU)—European Regional Development Fund within the Operational Programme for Strengthening Regional Development Potentials for Period 2007–2013.
- Berg, D., & Aas, K. (2009). Models for construction of multivariate dependance: A comparison study. European Journal of Finance, 15(7–8), 639–659.Google Scholar
- Bohanec, M. (2012). DEXi: Program for multi-attribute decision making: User’s manual: version 3.04. IJS Report DP-11153, Jožef Stefan Institute, Ljubljana.Google Scholar
- Bohanec, M., & Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica, 1, 145–157.Google Scholar
- Boškoski, P., Petrovčič, J., Musizza, B., & Juričić, Ð. (2011). An end-quality assessment system for electronically commutated motors based on evidential reasoning. Expert Systems with Applications, 38(11), 13,816–13,826.Google Scholar
- Bouyé, E., Durrleman, V., Riboulet, A. N. G., & Roncalli, T. (2000). Copulas for finance—A reading guide and some applications. http://ssrn.com/abstract=1032533.
- Brent, R. (1993). Algorithms for minimization without derivatives. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Crabtree, C. J. (2010). Survey of commercially available condition monitoring systems for wind turbines. Tech. rep.: Durham University, School of Engineering and Computing Science.Google Scholar
- Despa, S. (2007). Quantile regression. http://www.cscu.cornell.edu/news/statnews/stnews70.pdf.
- Forsythe, G., Malcolm, M., & Moler, C. (1976). Computer Methods for Mathematical Computations. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Gasar, S., Bohanec, M., & Rajkovič, V. (2003). A combined data mining and decision support approach to educational planning. In D. Mladenić, N. Lavrač, M. Bohanec, & S. Moyle (Eds.), Data mining and decision suport Integration and collaboration. Norwell, MA: Kluwer.Google Scholar
- Hofert, M. (2010). Construction and sampling of nested archimedean copulas. In Copula theory and its applications, proceedings of the workshop held in Warsaw 25–26 September, 2009, Lecture Notes in, Statistics (pp. 147–160). Berlin: Springer.Google Scholar
- Jaimungal, S., & Ng, E. K. (2009). Kernel-based copula processes. In ECML PKDD, 2009 (pp. 628–643).Google Scholar
- Kim, J. M., Jung, Y. S., Sungur, E. A., Han, K. H., Park, C., & Sohn, I. (2008). A copula method for modeling directional dependence of genes. BMC Bioinformatics, 9. doi: 10.1186/1471-2105-9-225.
- Mercier, G., Moser, G., & Serpico, S. B. (2008). Conditional copulas for change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1428–1441.Google Scholar
- Mileva-Boshkoska, B., & Bohanec, M. (2011). Ranking of qualitative decision options using copulas. In D. Klatte, H. J. Lüthi, & K. Schmedders (Eds.), Operations research proceedings.Google Scholar
- Nelsen, R. B. (2006). An introduction to copulas (2nd ed.). New York: Springer.Google Scholar
- Pavlovič, M., Čerenak, A., Pavlovič, V., Rozman, Č., Pažek, K., & Bohanec, M. (2011). Development of DEX-HOP multi-attribute decision model for preliminary hop hybrids assessment. Computers and Electronics in Agriculture, 75, 181–189. Google Scholar
- Peng, Z., & Chu, F. (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 18, 199–211.Google Scholar
- Rachev, S. T. (Ed.). (2003). Handbook of heavy tailed distributions in finance. North Holland: Elsevier.Google Scholar
- Röpke, K., & Filbert, D. (1994). Unsupervised classification of universal motors using modern clustering algorithms. In Proceedings of the SAFEPROCESS’94, IFAC symposium on fault detection, supervision and technical processes II (pp. 720–725).Google Scholar
- Sasi, B., Payne, A., York, B., Gu, A., & Ball, F. (2001). Condition monitoring of electric motors using instantaneous angular speed. In Paper presented at the maintenance and reliability conference (MARCON), Gatlinburg, TN.Google Scholar
- Sklar, A. (1996). Distributions with fixed marginals and related topics—Random variables, distribution functions, and copulas—A personal look backward and forward (Vol. 28). Hayward, CA: Institute of Mathematical Statistics.Google Scholar
- Walters, E. J., Morrell, C. H., & Auer, R. E. (2006). An investigation of the median-median method of linear regression. Journal of Statistics Education, 14(2). www.amstat.org/publications/jse/v14n2/morrell.html.