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Control Chart Pattern Recognition Based on Convolution Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)

Abstract

Quality control chart pattern recognition plays an extremely important role in controlling the products quality. By means of real-time monitoring control, the abnormal status of the product during production can be timely observed. A method of control pattern recognition based on convolution neural network is proposed. Firstly, the control chart patterns (CCPs) are analyzed, the statistical characteristics and shape features of the control charts are considered, and the appropriate characteristics to distinguish the different abnormal patterns are selected; secondly, deep learning convolution neural network is trained and learned; finally, the feasibility and effectiveness of the control chart pattern recognition are verified through Monte Carlo simulation.

Keywords

Quality control chart Pattern recognition Convolution neural Network Monte Carlo Deep learning 

Notes

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No: 61402361, 60903124); project supported by the scientific research project of Shaanxi Provincial Department of Education (Grant No: 14JK1521); Shaanxi province science and technology research and development project (Grant No: 2012KJXX-34).

References

  1. 1.
    Yang, W. A., Zhou, W.: Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines [J]. Neuro computing, Volume 147, 5 January, Pages 260–270 (2015).Google Scholar
  2. 2.
    Pham, D. T.: Estimation and generation of training patterns for control chart pattern Recognition [J]. Computers & Industrial Engineering, Volume 95, Pages 72–82 (2016).Google Scholar
  3. 3.
    Guo, X. J., Chen, L.: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis [J]. Measurement 93, 490–502 (2016).Google Scholar
  4. 4.
    Shi, Y., Shi, Y. Q.: Production pattern recognition based on the distributed LDA algorithm under the background of big data. [J]. Manufacturing Automation, Volume 39, 3, Pages 24–28 (2016).Google Scholar
  5. 5.
    Hassan A., Shariff Nabi Baksh, M., Shaharoun, A. M.: Improved SPC chart pattern recognition using statistical features [J]. International Journal of Production Research, 41(7):1587–1603 (2010).Google Scholar
  6. 6.
    Lei, H., Govindaraju, V.: Half-Against-Half Multi-class Support Vector Machines [C]. The 6th International Workshop on Multiple. Classifier Systems, Monterrey, CA, June, 156–164 (2005).Google Scholar
  7. 7.
    Yang, Y. S., Wu, D. H., Su, H. T.: Abnormal Pattern Recognition Method for Control Chart Based on Principal Component Analysis and Support Vector Machine [J]. Journal of System Simulation. 18(5):1314–1318 (2006).Google Scholar
  8. 8.
    Lee, H., Grosse, R., Ranganath, R.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations [C]. Proceedings of the 26th Annual International Conference on Machine Learning. New York, USA, ACM, 609–628 (2009).Google Scholar
  9. 9.
    Gauri, S. K., Chakraborty, S.: Feature-based recognition of control chart patterns [J] Computers and Industrial Engineering, 51, 726–742 (2006).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Mechanical and Precision Instrument EngineeringXi’an University of TechnologyXi’anChina

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