Abnormal data analysis in process industries using deep-learning method

Wen Song, Wei Weng, Shigeru Fujimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PublisherIEEE Computer Society
Pages2356-2360
Number of pages5
Volume2017-December
ISBN (Electronic)9781538609484
DOIs
Publication statusPublished - 2018 Feb 9
Event2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore
Duration: 2017 Dec 102017 Dec 13

Other

Other2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
CountrySingapore
CitySingapore
Period17/12/1017/12/13

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Keywords

  • Data analysis
  • deep-learning
  • wavelet-denoising

ASJC Scopus subject areas

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

Cite this

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