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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.
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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
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