The Method of Early Real-Time Fault Diagnosis for Technical Process Based on Neural Network

By taking the process of synthetic ammonia decarbornization as the research object, a new method of early real-time fault diagnosis based on the linear classifier-reforming neural network was proposed. The method, which need not establish accurate mathematical model, and has the advantages of its si...

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Bibliographic Details
Published in2010 Third International Symposium on Intelligent Information Technology and Security Informatics pp. 653 - 657
Main Authors Yingjun Guo, Lihua Sun, Haichao Ran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Clustering algorithms
Fault detection
Fault diagnosis
Information technology
Intelligent networks
Mathematical model
neural network
Neural networks
Product safety
Production
safety production
synthetic ammonia decarbornization
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Summary:By taking the process of synthetic ammonia decarbornization as the research object, a new method of early real-time fault diagnosis based on the linear classifier-reforming neural network was proposed. The method, which need not establish accurate mathematical model, and has the advantages of its simple learning algorithm, accumulate knowledge from example automatically, learning and classification of parallel processing and fast response speed etc. The results show that it can be applied to early real-time fault diagnosis in the process, and can provide techniques guarantee for safety production.
ISBN:9781424467303
1424467306
DOI:10.1109/IITSI.2010.92