A Hybrid Model for Fault Diagnosis Using Model Based Approaches and Support Vector Machine

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

Chang Jun Lee¹  and En Sup Yoon¹  Chonghun Han¹  Gibaek Lee² 

[1] School of Chemical and Biological Engineering, Seoul National University;Department of Chemical and Biological Engineering, Chungju National University

关键词: Support Vector Machine;    Signed Diagraph;    System Decomposition;    Fault Diagnosis;    Process Monitoring;   

DOI  :  10.1252/jcej.39.1085

来源: Maruzen Company Ltd

【 摘 要 】

References(22)Cited-By(1)The procedure followed in chemical processes can be expressed in simple terms such as the flow of events from the raw materials to the product. To obtain the best final product, chemical engineers have to consider many factors including environmental effects, stability, economic considerations, and so on. In particular, when considering the stability if the process and the purity of the product, it is very important to detect any faults in the chemical process immediately.In this paper, a hybrid fault diagnosis model based on the signed digraph (SDG) and support vector machine (SVM) is proposed. By means of the system decomposition based on SDG, the local models of each measured variable are constructed and more accurate and fast models are using an SVM, which has no loss of information and shows good performance, in order to obtain the estimated value of the variable, which is then compared with the measured value in order to diagnose the fault. To verify the performance of the proposed model, the Tennessee Eastman (TE) Process was studied and the proposed method was found to demonstrate a good diagnosis capability compared with previous statistical methods.

【 授权许可】

Unknown