Abstract
The rapid development and adoption of sensors and data storage solutions such as IOT (Internet of Things) has enabled the collection of a large amount of data in production facilities. The data may come from different sources such as process parameters and quality characteristics. However, traditional statistical process control (SPC) tools were not built to take the full advantages of the data provided. Traditional SPC tools such as control charts are often applied to critical quality characteristics (QCs) on a product rather than incorporating process parameters associated with the critical QCs. This chapter proposes a method that is capable of monitoring all process and quality data simultaneously. The proposed method adopts precontrol and group control chart ideas to pinpoint change location and timeframe in a production system. After the change location and timeframe have been identified, more elaborate models or data analytics methods can be used to identify potential assignable causes. Simulation studies are conducted to establish the properties of the proposed method. Guidelines are provided to help users how to implement the proposed method in any production facility including those facing big data issues.
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References
Shewhart, W. A. (1930). Economic quality control of manufactured product. Bell System Technical Journal, 9(2), 364–389.
Runger, G. C., & Testik, M. C. (2004). Multivariate extensions to cumulative sum control charts. Quality and Reliability Engineering International, 20(6), 587–606.
Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53.
Hu, J., Runger, G., & Tuv, E. (2007). Tuned artificial contrasts to detect signals. International Journal of Production Research, 45(23), 5527–5534.
Hotelling, H. (1947). Multivariate quality control—Illustrated by air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). New York: McGraw Hill.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
Montgomery, D. C. (2009). Introduction to statistical quality control (6th ed.). Hoboken, NJ: Wiley.
Pignatiello, J. J., & Runger, G. C. (1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22, 173–186.
Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality control schemes. Technometrics, 30, 291–303.
Jiang, Q., & Yan, X. (2014). Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability and Bayesian interference. Chemometrics and Intelligent Laboratory Systems, 136, 121–137.
Tong, C., & Yan, X. (2017). A novel decentralized process monitoring scheme using a modified multiblock PCA algorithm. IEEE Transactions on Automation Science and Engineering, 14, 1129–1138.
Liu, Y., Pan, Y., Wang, Q., & Huang, D. (2015). Statistical process monitoring with integration of data projection and one-class classification. Chemometrics and Intelligent Laboratory Systems, 149, 1–11.
Zheng, Y., Wang, Y., Wong, D. S. H., & Wang, Y. (2015). A time series model coefficients monitoring approach for controlled processes. Chemical Engineering Research and Design, 100, 228–236.
Jiang, Q., Huang, B., & Yan, X. (2016). GMM and optimal principal components-based Bayesian method for multimode fault diagnosis. Computers and Chemical Engineering, 84, 338–349.
Gajjar, S., Kulachi, M., & Palazoglu, A. (2016). Use of sparse principal component analysis (SPCA) for fault detection. IFAC-PapersOnLine, 49, 693–698.
Gajjar, S., & Palazoglu, A. (2016). A data-driven multidimensional visualization technique for process fault detection and diagnosis. Chemometrics and Intelligent Laboratory Systems, 154, 122–136.
Yan, J., Chen, C. Y., Yao, Y., & Huang, C. C. (2016). Robust multivariate statistical process monitoring via stable principal component pursuit. Industrial and Engineering Chemistry Research, 55, 4011–4021.
Jiang, Q., Yan, X., & Huang, B. (2016). Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference. IEEE Transactions on Industrial Electronics, 63, 377–386.
Boyd, D. F. (1950). Applying the group control chart for –x and R. Industrial Quality Control, 7(3), 22–25.
Lublinsky, B., Smith, K., & Yakubovich, A. (2013). Professional Hadoop solutions (pp. 63–97). Indianapolis, IN: Wiley.
Rajaraman, A., & Ullman, J. D. (2012). Mining of massive datasets (pp. 21–42). Cambridge: Cambridge Printing House.
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Koppel, S., I Chang, S. (2019). Statistical System Monitoring (SSM) for Enterprise-Level Quality Control. In: Lio, Y., Ng, H., Tsai, TR., Chen, DG. (eds) Statistical Quality Technologies. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-20709-0_3
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DOI: https://doi.org/10.1007/978-3-030-20709-0_3
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