Masoud Sadeghian and Alireza Fatehi
Identification, Prediction and Detection of the Process Fault in a Cement Rotary Kiln by Locally Linear NeuroFuzzy Technique
1367 - 1373
2009
3
10
International Journal of Mechanical and Mechatronics Engineering
https://publications.waset.org/pdf/3445
https://publications.waset.org/vol/34
World Academy of Science, Engineering and Technology
In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an inputoutput model is identified for the plant. To identify the various operation points in the
kiln, Locally Linear NeuroFuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure
algorithm. Then, by using this method, we obtained 3
distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes
prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented.
At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
Open Science Index 34, 2009