A Fault Diagnosis Bayesian Network Model for Cement Rotary Kiln
LIU Hao-ran1, LI Xuan2 , MA Ming2，LI Shi-zhao2
1. Information Science and Engineering College of Yanshan University, Hebei Province Key Laboratory of Special Optical Fiber and Optical Fiber Sensing, Qinhuangdao, Hebei 066004, China；
2. Information Science and Engineering College of Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：In order to realize fault diagnosis of the cement rotary kiln, Bayesian Network was used to establish the model of rotary kiln intelligent diagnosis. In the process of building model, an improved Bayesian Network structure learning algorithm was proposed. The improved algorithm requires dataset but doesn51 rely on prior knowledge. Based on the Bayesian Network established by the improved algorithm, Maximum likelihood estimation ( MLE) algorithm and variable elimination method are used to complete parameter study and diagnosis reasoning. To testify accuracy and feasibility of cement rotary kiln fault diagnosis Bayesian Network model, plenty of experiments were conducted with field data.
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2014, Vol. 35 
