A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In th...

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Bibliographic Details
Published in:Canadian journal of chemical engineering 2020-06, Vol.98 (6), p.1280-1292
Main Authors: Gao, Xinrui, Yang, Fan, Feng, Enbo
Format: Article
Language:English
Subjects:
Artificial neural networks
convolutional neural network
Dynamic characteristics
dynamic feature extraction
Fault detection
Fault diagnosis
Feature extraction
Neural networks
Process controls
TE process
Time
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Summary:Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi‐time scale dynamic feature extraction is proposed. The CNN‐based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.
ISSN:0008-4034
1939-019X
DOI:10.1002/cjce.23740