Fault Detection and Isolation of a cement rotary kiln using fuzzy clustering algorithm

Author

Talebnezhad, Nayereh ; Fatehi, A. ; Shoorehdeli, Mahdi Aliyari

Author_Institution

Fac. of Grad. Studies, Islamic Azad Univ., Tehran, Iran

fYear

2013

fDate

27-29 Aug. 2013

Firstpage

1

Lastpage

6

Abstract

In this paper, Fault Detection and Isolation (FDI) is studied for the rotary kiln of Saveh White Cement Company. To do so, K-means algorithm as a crisp clustering, Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms as fuzzy clustering are used. In those, for finding number of clusters, Cluster Validity Indices (CVI) are applied. Principal Component Analysis (PCA) mapped the clusters into two dimensional spaces. Fault detection and isolation performance are evaluated by three criteria namely sensitivity, specificity, and confusion matrix. The results reveal that GK fuzzy algorithm provides better performance on detection and isolation of fault in this industrial plant.

Keywords

cement industry; fault diagnosis; fuzzy set theory; kilns; pattern clustering; principal component analysis; production engineering computing; CVI; FCM algorithm; FDI; GK algorithm; Gustafson-Kessel algorithm; PCA; Saveh White Cement Company; cement rotary kiln; cluster validity indices; confusion matrix criteria; crisp clustering; fault detection and isolation; fuzzy c-means algorithm; fuzzy clustering; fuzzy clustering algorithm; industrial plant; k-means algorithm; principal component analysis; sensitivity criteria; specificity criteria; Cement Rotary Kiln; Clustering Algorithms; Fault Detection and Isolation;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (IFSC), 2013 13th Iranian Conference on

Conference_Location

Qazvin

Print_ISBN

978-1-4799-1227-8

Type

conf

DOI

10.1109/IFSC.2013.6675593

Filename

6675593