采用改进闪电搜索算法的冷水机组故障特征选择研究  

Research on fault feature selection of the chiller using the improved lightning search algorithm

作　　者：王华秋 兰群 赵利军 WANG Huaqiu;LAN Qun;ZHAO Lijun(School of Liangjiang Artificial Intelligence,Chongqing University of Technology,Chongqing 401135,China;Anyang Cigarette Factory,China Tobacco Henan Industrial Co.,Ltd.,Anyang 455000,China)

机构地区：[1]重庆理工大学两江人工智能学院,重庆401135 [2]河南中烟工业有限责任公司安阳卷烟厂,河南安阳455000

出　　处：《重庆理工大学学报（自然科学）》2023年第1期140-148,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：教育部科技项目(2018YFB1700803)。

摘　　要：提出了一种用于冷水机组故障特征选择的方法,先使用Fisher Score剔除少数对故障类别极不敏感的特征,再利用改进的闪电搜索算法确定特征的权重以及应选个数,从而得到最终的特征子集。在ASHRAE 1043RP数据上进行实验,得到了包含13个参数的冷水机组故障特征子集且大部分是温度参数。采用最近邻算法(k-nearest neighbors, KNN)、随机森林(random forest, RF)、BP(back bropagation)神经网络和门控循环单元(gated recurrent unit, GRU)4种方法求出了每类故障的诊断准确率,与原始数据相比,部分故障诊断精度也有所提高,验证了所选的特征子集的有效性。This paper proposes a method for fault feature selection of chillers. Firstly, the Fisher Score is used to eliminate a few features that are extremely insensitive to fault categories, and then the improved Lightning Search Algorithm is used to determine the weight of features and the number of features that should be selected. Thus, the final chiller feature subset is obtained. Experiments are carried out on ASHRAE Research Project 1 043 data, and a subset of chiller fault features containing 13 parameters is obtained, most of which are temperature parameters. Furthermore, four methods including k-Nearest neighbors(KNN), random forest(RF), BP neural network and gated recurrent unit(GRU) are used to obtain the diagnostic accuracy of each fault. Partial fault diagnosis accuracy is also improved compared with the original data, verifying the effectiveness of the selected feature subset.

关 键 词：冷水机组 故障特征子集 改进的闪电搜索算法 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]