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Author:

Zhang, Haili (Zhang, Haili.) | Wang, Pu (Wang, Pu.) | Gao, Xuejin (Gao, Xuejin.) (Scholars:高学金) | Qi, Yongsheng (Qi, Yongsheng.) | Gao, Huihui (Gao, Huihui.)

Indexed by:

Scopus SCIE

Abstract:

Fault detection and diagnosis (FDD) have been major concerns in abnormal event management of chemical processes for decades. Frequency-wise variations in chemical processes are not considered in most traditional methods, which affects the monitoring performance. An amplitude-frequency images-based convolutional neural network (ConvNet) is proposed for FDD in chemical processes. The fast Fourier transform (FFT) is first performed on data slice collected within a period to extract both amplitude-wise dynamics and frequency-wise variations, with the results in images. Then, the amplitude-frequency images are fed into ConvNet for FDD. ConvNet is applied as a binary classifier, in which each classifier corresponds to only one fault. Thus, an expandable framework is provided to incorporate a new fault. The performance of the proposed amplitude-frequency images-based ConvNet in FDD is demonstrated in a numerical case and the Tennessee Eastman process.

Keyword:

amplitude-frequency image fault detection and diagnosis chemical process convolutional neural network fast Fourier transform

Author Community:

  • [ 1 ] [Zhang, Haili]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Pu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Gao, Xuejin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Gao, Huihui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Haili]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 6 ] [Wang, Pu]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 7 ] [Gao, Xuejin]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 8 ] [Gao, Huihui]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 9 ] [Zhang, Haili]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
  • [ 10 ] [Wang, Pu]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
  • [ 11 ] [Gao, Xuejin]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
  • [ 12 ] [Gao, Huihui]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
  • [ 13 ] [Zhang, Haili]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 14 ] [Wang, Pu]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 15 ] [Gao, Xuejin]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 16 ] [Gao, Huihui]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 17 ] [Qi, Yongsheng]Inner Mongolia Univ Technol, Sch Elect Power, Hohhot 010051, Peoples R China

Reprint Author's Address:

  • 高学金

    [Gao, Xuejin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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Related Keywords:

Source :

JOURNAL OF CHEMOMETRICS

ISSN: 0886-9383

Year: 2019

Issue: 9

Volume: 33

2 . 4 0 0

JCR@2022

ESI Discipline: CHEMISTRY;

ESI HC Threshold:166

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 13

SCOPUS Cited Count: 16

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

Affiliated Colleges:

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