A Biochemical Fault Detection Method Based on Stack Noise Reduction Sparse Automatic Encoder

A method based on stack noise reduction sparse automatic coder is proposed for biochemical process fault detection. Based on SDSA, softmax classifier is introduced to build a deep neural network model, and particle swarm optimization algorithm is used to optimize the parameters of the model to impro...

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Bibliographic Details
Published in:Chinese Control and Decision Conference pp. 5344 - 5349
Main Authors: Wang, Ping, Chu, Zhigang, Sun, Lupeng
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2020
Subjects:
Biological system modeling
Classification algorithms
Deep Neural Network
Encoding
Fault detection
Noise reduction
Stacked Denoising Sparse Autoencoder
Support vector machines
Training
ISSN:1948-9447
Online Access:Get full text
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Summary:A method based on stack noise reduction sparse automatic coder is proposed for biochemical process fault detection. Based on SDSA, softmax classifier is introduced to build a deep neural network model, and particle swarm optimization algorithm is used to optimize the parameters of the model to improve the sensitivity of the model in fault detection. The effectiveness of the proposed method is verified by the simulation of Eastman process in Tennessee.
ISSN:1948-9447
DOI:10.1109/CCDC49329.2020.9164763