基于Bayesian改进算法的回转窑故障诊断模型研究

A study on the fault diagnosis model of rotary kiln based on an improved algorithm of Bayesian

贝叶斯网络是数据挖掘最有效和可靠的方法之一,而贝叶斯网络结构学习是贝叶斯网络研究的关键环节。针对现有经典结构学习算法——爬山算法易陷入局部最优、效率低的问题,通过计算互信息建立最大支撑树,并将最大支撑树与简化爬山算法相结合,提出了一种新的贝叶斯网络结构学习改进算法。通过与经典的爬山法和K2算法进行比较,结果表明该改进算法不仅能够得到较高准确率的模型,而且能够提高模型建立的效率。最后基于该改进算法,结合冀东水泥集团的水泥回转窑现场运行数据,建立了水泥回转窑故障诊断模型,实现了精确快速的故障诊断。

Bayesian network is one of the most effective 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 existing classic Hill-Climbing algorithm easily falling into local optimum and low in efficiency, the Most Weight Supported Tree by calculating the mutual information is established. Combining the Most Weight Supported Tree and the simplified Hill-Climbing algorithm, a new improved Bayesian Network structure learning algorithm is proposed. Compared with the classic Hill-Climbing algorithm and K2 algorithm, the result shows that the 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.