Investigation of Fault Diagnosis Model of Rotary Kiln Based on Improved Algorithm of Bayesian

Bayesian Network is one of the most efficient and reliable method in data mining, and Bayesian Network structure learning is a key link in the process of Bayesian Network research. Aiming at the problem of the classic Hill-Climbing algorithm is easy to fall into local optimum and low in efficiency, establishing the Most Weight Supported Tree by calculating the mutual information, and combining the Most Weight Supported Tree and the simplified Hill-Climbing algorithm, proposes a new improved Bayesian Network structure learning algorithm. Comparing with the classic Hill-Climbing algorithm and K2 algorithm, the simulation experiments shown that the improved algorithm not only can obtain a high accuracy rate model, but improve the efficiency of building model. Based on the improved algorithm and combined with JiDong cement´s cement rotary kiln operating data, we can establish the fault diagnosis model of cement rotary kiln and realize a precise and rapid fault diagnosis.