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An effective combined multivariate control chart based on support vector data description

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Abstract

Conventional multivariate control charts usually focus on a specific process shifts range (small or large), and they cannot get the knowledge of manufacturing process through the learning of in-control data and be effective over the whole range of mean shifts, due to the characteristics of their own structures. In this paper, an effective combined multivariate control chart (named CDD chart, i.e., combined D-MCUSUM and D chart) with an adaptive control limit is proposed to improve the overall detection ability of monitoring techniques in multivariate statistical process control. Besides, this paper also provides a basic methodology for designing the adaptive control limit and recommended values of some key parameters (e.g. window size) for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the proposed control chart. Simulation results show that the CDD chart offers a better overall performance, compared with regular control charts (e.g. MCUSUM). In addition, a real industrial case also illustrates the effectiveness of the proposed control chart in applications.

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References

  • Abedinia O, Bekravi M, Ghadimi N (2017) Intelligent controller based wide-area control in power system. Int J Uncertain Fuzziness Knowl Based Syst 25:1–30

    Article  Google Scholar 

  • Abujiya MaR, Riaz M, Lee MH (2013) Improving the performance of combined Shewhart–Cumulative sum control charts. Qual Reliab Eng Int 29:1193–1206

    Article  Google Scholar 

  • Ahmadian I, Abedinia O, Ghadimi N (2014) Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front Energy 8:412–425

    Article  Google Scholar 

  • Alt FB (1985) Multivariate quality control. Wiley StatsRef: Statistics Reference Online, vol 6, pp 110–122

  • Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control charts: an overview. Qual Reliab Eng Int 23:517–543

    Article  Google Scholar 

  • Chen R, Liang C-Y, Hong W-C, Gu D-X (2015) Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl Soft Comput 26:435–443

    Article  Google Scholar 

  • Cortez CAC, Pinto JC (2017) Improvement of Karush–Kuhn–Tucker conditions under uncertainties using robust decision making indexes. Appl Math Model 43:630–646

    Article  MathSciNet  Google Scholar 

  • Crosier RB (1988) Multivariate generalizations of cumulative sum quality-control schemes. Technometrics 30:291–303

    Article  MathSciNet  Google Scholar 

  • Eseye AT, Zhang J, Zheng D, Li H, Jingfu G (2017a) A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction. In: Cloud computing and big data analysis (ICCCBDA), 2017 IEEE 2nd international conference on, 2017a IEEE, pp 489–493

  • Eseye AT, Zhang J, Zheng D, Li H, Jingfu G (2017b) Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANFIS approach. In: Cloud computing and big data analysis (ICCCBDA), 2017 IEEE 2nd international conference on, 2017b. IEEE, pp 499–503

  • Eseye AT, Zhang J, Zheng D, Ma H, Jingfu G (2017c) A double-stage hierarchical ANFIS model for short-term wind power prediction. In: Big data analysis (ICBDA), 2017 IEEE 2nd international conference on, 2017c. IEEE, pp 546–551

  • Eseye AT, Zhang J, Zheng D, Ma H, Jingfu G (2017d) Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANN approach. In: Big data analysis (ICBDA), 2017 IEEE 2nd international conference on, 2017d. IEEE, pp 552–556

  • Eseye AT, Zhang J, Zheng D (2018) Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and meteorological information. Renew Energy 118:357–367

    Article  Google Scholar 

  • Gani W, Taleb H, Limam M (2011) An assessment of the kernel-distance-based multivariate control chart through an industrial application. Qual Reliab Eng Int 27:391–401

    Article  Google Scholar 

  • Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electric Power Energy Syst 104:423–435

    Article  Google Scholar 

  • García-Escudero LA, Duque-Perez O, Fernandez-Temprano M, Morinigo-Sotelo D (2017) Robust detection of incipient faults in VSI-fed induction motors using quality control charts. IEEE Trans Ind Appl 53:3076–3085

    Article  Google Scholar 

  • Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2017) Application of a new hybrid forecast engine with feature selection algorithm in a power system. Int J Ambient Energy. https://doi.org/10.1080/01430750.2017.1412350

    Article  Google Scholar 

  • Guh R-S, Shiue Y-R (2005) On-line identification of control chart patterns using self-organizing approaches. Int J Prod Res 43:1225–1254

    Article  Google Scholar 

  • He SG, Zhang CY (2011) Support vector data description based multivariate cumulative sum control chart. Adv Mater Res 314–316:2482–2485

    Article  Google Scholar 

  • Hotelling H (1947) Multivariate quality control, illustrated by the air testing of sample bombsights. Techniques of statistical analysis, McGraw Hill, New York, pp 111–184

    Google Scholar 

  • Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65

    Article  Google Scholar 

  • Kumar S, Choudhary A, Kumar M, Shankar R, Tiwari M (2006) Kernel distance-based robust support vector methods and its application in developing a robust K-chart International. J Prod Res 44:77–96

    Article  Google Scholar 

  • Lazzaretti AE, Tax DMJ, Neto HV, Ferreira VH (2016) Novelty detection and multi-class classification in power distribution voltage waveforms. Expert Syst Appl 45:322–330

    Article  Google Scholar 

  • Liang W, Pu X, Xiang D (2017) A distribution-free multivariate CUSUM control chart using dynamic control limits. J Appl Stat 44(11):2075–2093

    Article  MathSciNet  Google Scholar 

  • Liu Z, Hajiali M, Torabi A, Ahmadi B, Simoes R (2018) Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting. J Ambient Intell Human Comput 9(6):1919–1931

    Article  Google Scholar 

  • Lowry CA, Woodall WH, Champ CW, Rigdon SE (1992) A multivariate exponentially weighted moving average control chart. Technometrics 34:46–53

    Article  Google Scholar 

  • Nelson LS (1999) Technical aids: notes on the Shewhart control chart. J Qual Technol 31:124

    Article  Google Scholar 

  • Ning X, Tsung F (2013) Improved design of kernel distance-based charts using support vector methods. IIE Trans 45:464–476

    Article  Google Scholar 

  • Noruzi A, Banki T, Abedinia O, Ghadimi N (2015) A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods. Complexity 21:100–110

    Article  MathSciNet  Google Scholar 

  • Page ES (1954) Continuous inspection schemes. Biometrika 41:100–115

    Article  MathSciNet  Google Scholar 

  • Peng Y, Xu L, Reynolds MR (2015) The design of the variable sampling interval generalized likelihood ratio chart for monitoring the process mean. Qual Reliab Eng Int 31:291–296

    Article  Google Scholar 

  • Pignatiello JJ, Runger GC (1990) Comparisons of multivariate CUSUM charts. J Qual Technol 22:173–186

    Article  Google Scholar 

  • Prabukumar M, Agilandeeswari L, Ganesan K (2017) An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0655-5

    Article  Google Scholar 

  • Shi D, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Process 21:1799–1814

    Article  Google Scholar 

  • Sukchotrat T, Kim SB, Tsung F (2009) One-class classification-based control charts for multivariate process monitoring. IIE Trans 42:107–120

    Article  Google Scholar 

  • Sun R, Tsung F (2003) A kernel-distance-based multivariate control chart using support vector methods. Int J Prod Res 41:2975–2989

    Article  Google Scholar 

  • Tax DM, Duin RP (1999) Support vector domain description. Pattern Recognit Lett 20:1191–1199

    Article  Google Scholar 

  • Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54:45–66

    Article  Google Scholar 

  • Wang L-y, Li L, Zhang Z-h (2016) Accurate descriptions of hot flow behaviors across β transus of Ti-6Al-4 V alloy by intelligence algorithm GA-SVR. J Mater Eng Perform 25:3912–3923

    Article  Google Scholar 

  • Woodall WH, Ncube MM (1985) Multivariate CUSUM quality-control procedures. Technometrics 27:285–292

    Article  MathSciNet  Google Scholar 

  • Wu Z, Yang M, Jiang W, Khoo MB (2008) Optimization designs of the combined Shewhart-CUSUM control charts. Comput Stat Data Anal 53:496–506

    Article  MathSciNet  Google Scholar 

  • Yang W-A, Zhou W, Liao W, Guo Y (2015) Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines. Neurocomputing 147:260–270

    Article  Google Scholar 

  • Yin S, Ding SX, Xie X, Luo H (2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron 61:6418–6428

    Article  Google Scholar 

  • Yu J, Xi L (2008) Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes. Int J Prod Res 46:5907–5933

    Article  Google Scholar 

  • Zhang J, Li Z, Chen B, Wang Z (2014) A new exponentially weighted moving average control chart for monitoring the coefficient of variation. Comput Ind Eng 78:205–212

    Article  Google Scholar 

  • Zheng D, Shi M, Wang Y, Eseye AT, Zhang J (2017) Day-ahead wind power forecasting using a two-stage hybrid modeling approach based on scada and meteorological information, and evaluating the impact of input-data dependency on forecasting accuracy. Energies 10:1988

    Article  Google Scholar 

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Acknowledgements

This project is supported by National Natural Science Foundation of China (Grant no. 71401098  and 71501125).

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Authors and Affiliations

  1. School of Management, Shanghai University, Shanghai, China

    Beixin Xia

  2. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China

    Zheng Jian

  3. College of Engineering, Shanghai Ocean University, No. 999 Huchenghua Rd, Pudong New Area, Shanghai, 201306, China

    Ningrong Tao

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  1. Beixin Xia
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  2. Zheng Jian
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  3. Ningrong Tao
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Correspondence to Ningrong Tao.

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Appendix

Appendix

See Tables 2 and 3.

Table 2 The shift magnitudes of different shifts vectors
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Table 3 Data set for the illustrative example and other related statistics
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Xia, B., Jian, Z. & Tao, N. An effective combined multivariate control chart based on support vector data description. J Ambient Intell Human Comput 10, 4819–4835 (2019). https://doi.org/10.1007/s12652-018-1168-6

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