Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique

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 input–output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, 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 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.