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Control chart pattern recognition using the convolutional neural network

  • Tao Zan
  • Zhihao LiuEmail author
  • Hui Wang
  • Min Wang
  • Xiangsheng Gao
Article
  • 51 Downloads

Abstract

Unnatural control chart patterns (CCPs) usually correspond to the specific factors in a manufacturing process, so the control charts have become important means of the statistical process control. Therefore, an accurate and automatic control chart pattern recognition (CCPR) is of great significance for manufacturing enterprises. In order to improve the CCPR accuracy, experts have designed various complex features, which undoubtedly increases the workload and difficulty of the quality control. To solve these problems, a CCPR method based on a one-dimensional convolutional neural network (1D-CNN) is proposed. The proposed method does not require to extract complex features manually; instead, it uses a 1D-CNN to obtain the optimal feature set from the raw data of the CCPs through the feature learning and completes the CCPR. The dataset for training and validation, containing six typical CCPs, is generated by the Monte-Carlo simulation. Then, the influence of the network structural parameters and activation functions on the recognition performance is analyzed and discussed, and some suggestions for parameter selection are given. Finally, the performance of the proposed method is compared with that of the traditional multi-layer perceptron method using the same dataset. The comparison results show that the proposed 1D-CNN method has obvious advantages in the CCPR tasks. Compared with the related literature, the features extracted by the 1D-CNN are of higher quality. Furthermore, the 1D-CNN trained with simulation dataset still perform well in recognizing the real dataset from the production environment.

Keywords

Control chart Pattern recognition Convolutional neural network Feature learning Deep learning 

Notes

Acknowledgements

This study is supported by National Natural Science Foundation of China (No. 51575014), Science and Technology Project of Beijing Municipal Commission of Education (KM201410005026) and National Fund for Studying Abroad (201806545032). Special thanks to Dr. Jifeng Liang of Tongyu Heavy Industry Co., Ltd for providing production data.

References

  1. Addeh, A., Khormali, A., & Golilarz, N. A. (2018). Control chart pattern recognition using RBF neural network with new training algorithm and practical features. ISA Transactions.  https://doi.org/10.1016/j.isatra.2018.04.020.Google Scholar
  2. Ajm, T., & Hulzebosch, A. A. (1996). Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. Computers and Electronics in Agriculture, 15(1), 41–55.Google Scholar
  3. Al-Assaf, Y. (2004). Recognition of control chart patterns using multiresolution wavelets analysis and neural networks. Computers & Industrial Engineering, 47(1), 17–29.Google Scholar
  4. Assaleh, K., & Al-Assaf, Y. (2005). Features extraction and analysis for classifying causable patterns in control charts. Computers & Industrial Engineering, 49(1), 168–181.Google Scholar
  5. Awadalla, M. H. A., & Sadek, M. A. (2012). Spiking neural network-based control chart pattern recognition. Alexandria Engineering Journal, 51(1), 27–35.Google Scholar
  6. Bag, M. (2012). An expert system for control chart pattern recognition. International Journal of Advanced Manufacturing Technology, 62(1–4), 291–301.Google Scholar
  7. Cheng, C. S. (1997). A neural network approach for the analysis of control chart patterns. International Journal of Production Research, 35(3), 667–697.Google Scholar
  8. Cheng, C., & Hubele, N. F. (1992). Design of a knowledge based expert system for statistical process control. Computers & Industrial Engineering, 22(4), 501–517.Google Scholar
  9. Cheng, Z., & Ma, Y. Z. (2008). A research about pattern recognition of control chart using probability neural network. In ISECS international colloquium on computing, communication, control, & management. IEEE.Google Scholar
  10. Davis, R. B., & Woodall, W. H. (1988). Performance of the control chart trend rule under linear shift. Journal of Quality Technology, 20(4), 260–262.Google Scholar
  11. Ducan, A. J. (1986). Quality control and industrial statistics (5th ed.). Homewood, IL: Richard D. Irwin.Google Scholar
  12. Gauri, S. K. (2010). Control chart pattern recognition using feature-based learning vector quantization. International Journal of Advanced Manufacturing Technology, 48(9–12), 1061–1073.Google Scholar
  13. Gauri, S. K., & Chakraborty, S. (2006). Feature-based recognition of control chart patterns. Computers & Industrial Engineering, 51(4), 726–742.Google Scholar
  14. Gauri, S. K., & Chakraborty, S. (2009). Recognition of control chart patterns using improved selection of features. Computers & Industrial Engineering, 56(4), 1577–1588.Google Scholar
  15. Guh, R. S. (2008). Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach. International Journal of Production Research, 46(14), 3959–3991.Google Scholar
  16. Guh, R. S., & Tannock, J. (1999). A neural network approach to characterize pattern parameters in process control charts. Journal of Intelligent Manufacturing, 10(5), 449–462.Google Scholar
  17. Hachicha, W., & Ghorbel, A. (2012). A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Oxford: Pergamon Press Inc.Google Scholar
  18. Hassan, A., Baksh, M., Shaharoun, A. M., & Jamaluddin, H. (2003). Improved SPC chart pattern recognition using statistical features. International Journal of Production Research, 41(7), 1587–1603.Google Scholar
  19. He, S., He, Z., & Wang, G. A. (2013). Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, 24(1), 25–34.Google Scholar
  20. Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., et al. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331–345.Google Scholar
  21. Jin, J., & Shi, J. (2001). Automatic feature extraction of waveform signals for in process diagnostic performance improvement. Journal of Intelligent Manufacturing, 12(3), 257–268.Google Scholar
  22. Kao, L. J., Lee, T. S., & Lu, C. J. (2016). A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine. Journal of Intelligent Manufacturing, 27(3), 653–664.Google Scholar
  23. Kuo, T., & Mital, A. (1993). Quality control expert systems: A review of pertinent literature. Journal of Intelligent Manufacturing, 4(4), 245–257.Google Scholar
  24. Liao, Y., Zeng, X., & Li, W. (2017). Wavelet transform based convolutional neural network for gearbox fault classification. In Prognostics and system health management conference (pp. 1–6). IEEE.Google Scholar
  25. Miao, Z., & Yang, M. (2019). Control chart pattern recognition based on convolution neural network. In B. Panigrahi, M. Trivedi, K. Mishra, S. Tiwari, & P. Singh (Eds.), Smart innovations in communication and computational sciences. Advances in intelligent systems and computing (Vol. 670). Singapore: Springer.Google Scholar
  26. Montgomery, D. C. (2007). Introduction to statistical quality control. London: Wiley.Google Scholar
  27. Nelson, L. S. (1984). The Shewhart control chart: Test for special causes. Journal of Quality Technology, 16(4), 237–239.Google Scholar
  28. Nelson, L. S. (1985). Interpreting Shewhart X-bar control charts. Journal of Quality Technology, 17(2), 114–116.Google Scholar
  29. Pelegrina, G. D., Duarte, L. T., & Jutten, C. (2016). Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods. Computers & Industrial Engineering, 92, 105–114.Google Scholar
  30. Pham, D. T., & Oztemel, E. (1994). Control chart pattern recognition using learning vector quantization networks. International Journal of Production Research, 32(3), 721–729.Google Scholar
  31. Pham, D. T., & Wani, M. A. (1997). Feature-based control chart pattern recognition. International Journal of Production Research, 35(7), 1875–1890.Google Scholar
  32. Ranaee, V., & Ebrahimzadeh, A. (2011). Control chart pattern recognition using a novel hybrid intelligent method. Applied Soft Computing Journal, 11(2), 2676–2686.Google Scholar
  33. Ranaee, V., & Ebrahimzadeh, A. (2013). Control chart pattern recognition using neural networks and efficient features: A comparative study. Pattern Analysis and Applications, 16(3), 321–332.Google Scholar
  34. Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–244.Google Scholar
  35. Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437.Google Scholar
  36. Swift, J. A. (1987). Development of a knowledge-based expert system for control-chart pattern recognition and analysis. Stillwater: Oklahoma State Universi.Google Scholar
  37. Western Electric Company. (1958). Statistical quality control handbook. Indianapolis: Western Electric Co., Inc.Google Scholar
  38. Xia, M., Li, T., Xu, L., Liu, L., & Silva, C. W. D. (2018). Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Transactions on Mechatronics, 23(1), 101–110.  https://doi.org/10.1109/TMECH.2017.2728371.Google Scholar
  39. Xie, Y., & Zhang, T. (2017). Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition. Shock and Vibration, 10, 15.  https://doi.org/10.1155/2017/3084197.Google Scholar
  40. Yang, W. A., & Zhou, W. (2015). Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble. Journal of Intelligent Manufacturing, 26(6), 1161–1180.Google Scholar
  41. 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(1), 260–270.Google Scholar
  42. Zan, T., Wang, M., & Fei, R. Y. (2010). Pattern recognition for control charts using AR spectrum and fuzzy ARTMAP neural network. Advanced Materials Research, 97–101, 3696–3702.Google Scholar
  43. Zhao, C., Wang, C., Hua, L., Liu, X., Zhang, Y., & Hu, H. (2017). Recognition of control chart pattern using improved supervised locally linear embedding and support vector machine. Procedia Engineering, 174, 281–288.Google Scholar
  44. Zhou, X., Jiang, P., & Wang, X. (2018). Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function. Journal of Intelligent Manufacturing, 12, 1–17.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Advanced Manufacturing TechnologyBeijing University of TechnologyBeijingChina

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