Abnormal data analysis in process industries using deep-learning method

Wen Song, Wei Weng, Shigeru Fujimura

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

This research is mainly about the abnormal data analysis in factories of process industries. In the processing factory, there are many sensors which transmit the values to each other. Workers in process factory need to be alerted when the values of some sensors are abnormal values. In our research, the main target is to detect the potential abnormal value from different sensors of process industries. Since the value is filled with noise and delays, we first use the cross-correlation and wavelet transformation to remove them. Then, use deep-learning method to train the model with processed data and use the model to detect potential abnormal value. Finally, we evaluate the model we trained by the data extracted from a real process factory. The result shows that our model performs well.

元の言語English
ホスト出版物のタイトル2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
出版者IEEE Computer Society
ページ2356-2360
ページ数5
2017-December
ISBN(電子版)9781538609484
DOI
出版物ステータスPublished - 2018 2 9
イベント2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore
継続期間: 2017 12 102017 12 13

Other

Other2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
Singapore
Singapore
期間17/12/1017/12/13

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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  • これを引用

    Song, W., Weng, W., & Fujimura, S. (2018). Abnormal data analysis in process industries using deep-learning method. : 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 (巻 2017-December, pp. 2356-2360). IEEE Computer Society. https://doi.org/10.1109/IEEM.2017.8290313