Abstract
The traditional statistical process control (SPC) technique is used as a monitoring tool to recognize mean shifts of discrete manufacturing processes. The fundamental assumption using SPC is the independence of observed process data. Such assumption is usually violated in practical industries, which proceeds the development of alternative schemes to monitor auto-correlated processes. This paper approaches a long short-term memory (LSTM)-based model to recognize mean shifts of auto-correlated processes with different autocorrelation coefficients and shift parameters. The performance of the proposed method is evaluated by an average run length of time series residual control chart in comparison with other monitoring schemes. It was observed that the LSTM-based monitoring approach could improve significantly upon the early detection of out-of-control operating cases in auto-correlated processes.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 51375290, 71777173), the Fundamental Research Funds for Central Universities and Shanghai Science.
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Chen, Sm., Yu, Jb. (2019). Mean Shifts Monitoring of Auto-correlated Manufacturing Processes Using Long Short-Term Memory-Based Recurrent Neural Network. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_10
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DOI: https://doi.org/10.1007/978-981-13-3402-3_10
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