A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm

被引:5
作者
Li, Zhangling [1 ,2 ]
Wang, Qi [1 ,2 ]
Xiong, Jianbin [1 ,2 ]
Cen, Jian [1 ,2 ]
Dai, Qingyun [3 ]
Liang, Qiong [1 ]
Lu, Tiantian [1 ,2 ]
机构
[1] Guangdong Polytech Normal Univ, Sch Automat, Guangzhou 510665, Peoples R China
[2] Guangzhou Intelligent Bldg Equipment Informat Inte, Guangzhou 510665, Peoples R China
[3] Guangdong Prov Key Lab Intellectual Property & Big, Guangzhou 510665, Peoples R China
基金
中国国家自然科学基金;
关键词
principal component analysis; improved sparrow search algorithm; random forest; building electrical system; fault diagnosis; INTELLIGENCE; CLASSIFICATION;
D O I
10.1088/1361-6501/ad2255
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Addressing the problems of manual dependence and low accuracy of traditional building electrical system fault diagnosis, this paper proposes a novel method, which is based on random forest (RF) optimized by improved sparrow search algorithm (ISSA-RF). Firstly, the method utilizes a fault collection platform to acquire raw signals of various faults. Secondly, the features of these signals are extracted by time-domain and frequency-domain analysis. Furthermore, principal component analysis is employed to reduce the dimensionality of the extracted features. Finally, the reduced features are input into ISSA-RF for classification. In ISSA-RF, the ISSA is used to optimize the parameters of the RF. The parameters for ISSA optimization are n_estimators and min_samples_leaf. In this case, the accuracy of the proposed method can reach 98.61% through validation experiment. In addition, the proposed method also exhibits superior performance compared with traditional fault classification algorithms and the latest building electrical fault diagnosis algorithms.
引用
收藏
页数:13
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