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作者:

Chen, Deyin (Chen, Deyin.) | Zan, Tao (Zan, Tao.) | Ma, Zhiqian (Ma, Zhiqian.) | Wang, Min (Wang, Min.) | Gao, Xiangsheng (Gao, Xiangsheng.) | Liu, Zhihao (Liu, Zhihao.)

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摘要:

This paper is aiming at the problem that the existing anomaly discrimination methods of control chart can not realize the discrimination of complex anomaly data and the low level of intelligence. It is exploring the performance of control chart pattern recognition based on deep learning. It briefly introduces 1DCNN, LSTM and BiLSTM then adopts a neural network model based on 1DCNN+BiLSTM. The Monte Carlo method is used to generate the simulation data of the control chart, and different abnormal data are generated by changing the parameters for simulation experiments. According to the effect of control chart pattern recognition under different abnormal data, the training samples are optimized and the parameters of the network model are determined. The results show that the proposed method performs better in recognition. © 2022 IEEE.

关键词:

Deep neural networks Pattern recognition Long short-term memory Control charts Flowcharting Monte Carlo methods

作者机构:

  • [ 1 ] [Chen, Deyin]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China
  • [ 2 ] [Zan, Tao]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China
  • [ 3 ] [Ma, Zhiqian]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China
  • [ 4 ] [Wang, Min]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China
  • [ 5 ] [Gao, Xiangsheng]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China
  • [ 6 ] [Liu, Zhihao]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Material and Manufacturing, Beijing, China

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年份: 2022

页码: 212-216

语种: 英文

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