改进的FP-growth算法及其在TE过程故障诊断中的应用

为了解决频繁模式增长( frequent pattern growth,FP-growth)算法因多次遍历频繁集列表而产生庞大频繁模式树需占用大量内存降低了运行效率的问题,提出一种改进的FP-growth( upgraded FP-growth,UFP)算法。首先,构造支持度函数实现各项与其支持度的映射,使算法的运行效率得到提高；其次,利用关键字筛选技术,把频繁项分成关键项表、非关键项表两部分,保证了最终获取的每条关联规则都是人们关注的有效信息；最后,根据频繁1-项集划分数据库子集并直接构造每一项的条件模式树,节省了内存空间。将UFP算法应用于Tenessee Eastman( TE)过程的故障诊断,通过与主成分分析( principal component analysis,PCA)、核主成分分析( kernel principal component analysis,KPCA)算法在多种故障下的诊断结果对比实验验证了算法的优越性。

Improved FP-growth Algorithm With Applications in TE Process Fault Diagnosis

FP-growth algorithm is very effective for mining frequent itemsets. However, the huge frequent pattern trees generated due to repeat F-list searching consume a large amount of memory and lead to low efficiency. In response to these drawbacks, this paper presented an improved algorithm, termed as UFP algorithm ( upgraded FP-growth) . First, it used support function to map the support rate with each item in order to improve the operating efficiency. Second, it took advantage of keyword filtering technology to devide the F-list into two parts, key-item list and non-critical list, ensuring the association rules which ultimately were obtained, were all valid information. Finally, it divided the whole database into subsets according to the first frequent itemsets and constructed condition pattern trees directly which saved lots of memory space. This paper applied UFP algorithm into TE process for fault diagnosis. The comparative experiments with PCA and KPCA algorithm under different process faults improve its superiority.