期刊
JOURNAL OF CHEMOMETRICS
卷 33, 期 9, 页码 -出版社
WILEY
DOI: 10.1002/cem.3168
关键词
amplitude-frequency image; chemical process; convolutional neural network; fast Fourier transform; fault detection and diagnosis
类别
资金
- China Postdoctoral Science Foundation [2017M620556, 2017 M620556]
- Beijing Municipal Commission of Education
- Natural Science Foundation of Beijing Municipality [4172007]
- National Natural Science Foundation of China [61640312, 61763037, 61803005]
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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