Mahalanobis-Taguchi system (MTS) is a kind of data mining and pattern recognition method which
can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance (MD)
measurement scale. In this paper, considering the influence of irregular distribution of the sample data and
abnormal variation of the normal data on accuracy of MTS, a feature recognition and selection model of the
equipment state based on the improved MTS is proposed, and two aspects of the model namely construction of
the original Mahalanobis space (MS) and determination of the threshold are studied. Firstly, the original training
sample space is statistically controlled by the X-bar-S control chart, and extreme data of the single characteristic
attribute is filtered to reduce the impact of extreme condition on the accuracy of the model, so as to construct
a more robust MS. Furthermore, the box plot method is used to determine the threshold of the model. And the
stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the
variation for the extreme condition which is identified as in the normal range. Finally, the improved model is
compared with the traditional one based on the unimproved MTS by using the data from the literature. The
result shows that compared with the traditional model, the accuracy and sensitivity of the improved model for
state identification can be greatly enhanced.
WANG Ning (王宁), ZHANG Zhuo (张卓)
. Feature Recognition and Selection Method of the Equipment State Based on Improved Mahalanobis-Taguchi System[J]. Journal of Shanghai Jiaotong University(Science), 2020
, 25(2)
: 214
-222
.
DOI: 10.1007/s12204-019-2107-1
[1] TAGUCHI G, JUGULUM R. The Mahalanobis-Taguchi strategy: A pattern technology system [M].Hoboken, NJ, USA: John Wiley & Sons, 2002.
[2] WOODALL W H, KOUDELIK R, TSUI K L, et al. A review and analysis of the Mahalanobis-Taguchi system[J]. Technometrics, 2003, 45(1): 1-15.
[3] YAZID A M, RIJAL J K, AWALUDDIN M S, et al. Pattern recognition on remanufacturing automotive component as support decision making using Mahalanobis-Taguchi system [J]. Procedia CIRP, 2015,26: 258-263.
[4] ABU M Y, JAMALUDIN K R, RAMLIE F. Pattern recognition using Mahalanobis-Taguchi system on connecting rod through remanufacturing process: A case study [J]. Advanced Materials Research, 2013, 845:584-589.
[5] MOHAN D, SAYGIN C, SARANGAPANI J. Realtime detection of grip length deviation during pull-type fastening: a Mahalanobis-Taguchi System (MTS)-based approach [J]. International Journal of Advanced Manufacturing Technology, 2008, 39(9/10): 995-1008.
[6] MAKAJIMA H, TAKADA K, YANO H, et al.Predictive evaluation and efficient management of medical examinations using Mahalanobis Taguchi system method [J]. Japanese Journal of Public Health,1999, 46(5): 351-363(in Japanese).
[7] DAS P, DATTA S. Exploring the effects of chemical composition in hot rolled steel product using Mahalanobis distance scale under Mahalanobis-Taguchi system[J]. Computational Materials Science, 2007, 38(4):671-677.
[8] CUDNEY E A, PARYANI K, RAGSDELL K M.Applying the Mahalanobis-Taguchi system to vehicle handling [J]. Concurrent Engineering, 2006, 14(4):343-354.
[9] ZENG J H, ZENG F Z. The measurement scale of Mahalanobis-Taguchi system optimization based on fuzzy robustness discriminant criterion [J]. Industrial Engineering and Management, 2008, 13(3): 52-55(in Chinese).
[10] NAKATSUGAWA M, OHUCHI A. A study on determination of the threshold in MTS algorithm [J]. Transactions of the Institute of Electronics Information and Communication Engineers A, 2001, 84(4): 519-527.
[11] SU C T, HSIAO Y H. An evaluation of the robustness of MTS for imbalanced data [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(10): 1321-1332.
[12] XU Q, ZHENG C D, HAN Z J. Study of Mahalanobis-Taguchi system in multiple recognition [J]. Journal of Nanjing University of Science and Technology, 2002,26(1): 92-95 (in Chinese).
[13] IQUEBAL A S, PAL A, CEGLAREK D, et al. Enhancement of Mahalanobis-Taguchi system via rough sets based feature selection [J]. Expert Systems with Applications, 2014, 41(17): 8003-8015.
[14] FOSTER C R, JUGULUM R, FREY D D. Evaluating an adaptive one-factor-at-a-time search procedure within the Mahalanobis-Taguchi system [J]. International Journal of Industrial and Systems Engineering,2009, 4(6): 600-614.
[15] RAI B K, CHINNAM R B, SINGH N. Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis [J]. International Journal of Industrial and Systems Engineering,2008, 3(2): 134-148.
[16] WANG N, SAYGIN C, SUN S D. Impact of Mahalanobis space construction on effectiveness of Mahalanobis-Taguchi system [J]. International Journal of Industrial and Systems Engineering, 2013,13(2): 233-249.
[17] LIPARAS D, ANGELIS L, FELDT R. Applying the Mahalanobis-Taguchi strategy for software defect diagnosis[J]. Automated Software Engineering, 2012,19(2): 141-165.
[18] LIPARAS D, LASKARIS N, ANGELIS L. Incorporating resting state dynamics in the analysis of encephalographic responses by means of the Mahalanobis-Taguchi strategy [J]. Expert Systems with Applications,2013, 40(7): 2621-2630.
[19] YANG T, CHENG Y T. The use of Mahalanobis-Taguchi System to improve flip-chip bumping height inspection efficiency [J]. Microelectronics Reliability,2010, 50(3): 407-414.
[20] GHASEMI E, AAGHAIE A, CUDNEY E A. Mahalanobis Taguchi system: A review [J]. International Journal of Quality & Reliability Management, 2015,32(3): 291-307.
[21] SOYLEMEZOGLU A, JAGANNATHAN S, SAYGIN C. Mahalanobis Taguchi system (MTS) as a prognostics tool for rolling element bearing failures [J]. Journal of Manufacturing Science and Engineering, 2010,132(5): 051014.
[22] JUGULUM R, TAGUCHI G, TAGUCHI S, et al. A review and analysis of the Mahalanobis-Taguchi system:Discussion [J]. Technometrics, 2003, 45(1): 16-21.
[23] GAUDARD M, RAMSEY P J. Introduction to statistical quality control [J]. Technometrics, 1997, 39(3):331-332.
[24] BENJAMINI Y. Opening the box of a boxplot [J]. The American Statistician, 1988, 42(4): 257-262.
[25] SU C T, HSIAO Y H. An evaluation of the robustness of MTS for imbalanced data [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(10): 1321-1332.
