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.
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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).
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Miao, Z., Yang, M. (2019). Control Chart Pattern Recognition Based on Convolution Neural Network. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_9
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DOI: https://doi.org/10.1007/978-981-10-8971-8_9
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