A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm
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...
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| Published in | Measurement science & technology Vol. 35; no. 5; p. 55110 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.05.2024
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| Online Access | Get full text |
| ISSN | 0957-0233 1361-6501 1361-6501 |
| DOI | 10.1088/1361-6501/ad2255 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Xiong, Jianbin Cen, Jian Dai, Qingyun Wang, Qi Li, Zhangling Lu, Tiantian Liang, Qiong |
| Author_xml | – sequence: 1 givenname: Zhangling orcidid: 0009-0007-8356-0263 surname: Li fullname: Li, Zhangling – sequence: 2 givenname: Qi orcidid: 0000-0002-6817-7967 surname: Wang fullname: Wang, Qi – sequence: 3 givenname: Jianbin orcidid: 0000-0002-2253-5546 surname: Xiong fullname: Xiong, Jianbin – sequence: 4 givenname: Jian orcidid: 0000-0002-1714-7397 surname: Cen fullname: Cen, Jian – sequence: 5 givenname: Qingyun orcidid: 0000-0002-7561-3704 surname: Dai fullname: Dai, Qingyun – sequence: 6 givenname: Qiong surname: Liang fullname: Liang, Qiong – sequence: 7 givenname: Tiantian orcidid: 0000-0002-1328-4984 surname: Lu fullname: Lu, Tiantian |
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| Cites_doi | 10.1016/j.cnsns.2013.12.012 10.1109/TIM.2020.3043873 10.1016/j.apacoust.2019.107020 10.1109/ PSGEC51302.2021.9541829 10.1109/AUTEEE48671.2019.9033363 10.1109/JAS.2021.1004129 10.1016/j.ymssp.2019.106587 10.1007/s11749-016-0481-7 10.1016/j.ijrefrig.2020.06.009 10.1016/j.segan.2021.100582 10.1016/j.neucom.2020.07.088 10.1049/iet-epa.2018.5274 10.1016/j.isatra.2021.02.042 10.1109/TIE.2017.2767540 10.1080/21642583.2019.1708830 10.1007/s10470-018-1351-x 10.1007/978-3-030-58728-4_16 10.1007/s00366-022-01604-x 10.1109/ ICICAS48597.2019.00104 10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00093 10.1007/s00521-017-2970-3 10.1088/1361-6501/ac86e5 10.1109/ICNN.1995.488968 10.1016/j.knosys.2021.106924 10.1016/j.ymssp.2015.08.030 10.1109/TII.2019.2915559 10.1016/j.enconman.2018.10.040 10.1023/A:1010933404324 10.1016/j.isatra.2016.10.014 10.1016/j.ymssp.2019.106609 10.1007/s00521-019-04612-z 10.1016/j.cosrev.2021.100378 10.1007/s00500-016-2474-6 10.1016/j.neucom.2018.05.002 10.1016/j.asej.2015.08.005 10.1016/j.ymssp.2018.02.016 10.1016/j.advengsoft.2013.12.007 10.1007/ 978-981-13-7403-6_11 10.1038/s41598-022-27031-y 10.1109/JSEN.2017.2726011 10.1007/s10470-019-01433-x 10.4236/jdaip.2020.84020 10.4249/scholarpedia.1883 10.1109/TEVC.2012.2196047 10.1109/TCYB.2021.3123667 10.1016/j.eswa.2013.02.018 10.1109/57.917529 |
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| References | Zhang (mstad2255bib24) 2021; 220 Anowar (mstad2255bib13) 2021; 40 Zhang (mstad2255bib3) 2022; 119 Zhu (mstad2255bib41) 2020; 32 Yan (mstad2255bib9) 2018; 313 Chandra Sen (mstad2255bib30) 2020; vol 937 Fang (mstad2255bib29) 2021 Xiong (mstad2255bib38) 2023; 13 Eberhart (mstad2255bib20) 1995; vol 4 Lei (mstad2255bib6) 2020; 138 Mirjalili (mstad2255bib22) 2014; 69 Zhang (mstad2255bib39) 2019; 98 Boateng (mstad2255bib33) 2020; 8 Song (mstad2255bib25) 2020 Breiman (mstad2255bib32) 2001; 45 Guo (mstad2255bib14) 2020; 118 Pan (mstad2255bib4) 2017; 65 Xu (mstad2255bib10) 2019 Wang (mstad2255bib35) 2017; 17 Wang (mstad2255bib21) 2018; 22 Chai (mstad2255bib47) 2019; 16 Xue (mstad2255bib11) 2017; 66 Helmi (mstad2255bib8) 2019; 13 Duval (mstad2255bib49) 2001; 17 Ahmad (mstad2255bib42) 2018; 30 Tang (mstad2255bib19) 2021; 8 Zhang (mstad2255bib40) 2019; 100 Liu (mstad2255bib1) 2019 Liu (mstad2255bib27) 2018; 108 Van der Maaten (mstad2255bib16) 2008; 9 Peterson (mstad2255bib28) 2009; 4 Xue (mstad2255bib23) 2020; 8 Jiao (mstad2255bib5) 2020; 417 Adhya (mstad2255bib2) 2022; 29 Ghojogh (mstad2255bib12) 2019 Seyyedabbasi (mstad2255bib26) 2023; 39 Xiong (mstad2255bib37) 2022; 33 Valdez (mstad2255bib18) 2021; vol 915 Biau (mstad2255bib34) 2016; 25 Sharma (mstad2255bib7) 2020; 158 Li (mstad2255bib45) 2014; 19 Volkan Pehlivanoglu (mstad2255bib44) 2012; 17 Cerrada (mstad2255bib48) 2016; 70 Zhang (mstad2255bib15) 2020; 70 Ramesh Babu (mstad2255bib31) 2017; 8 Hu (mstad2255bib36) 2020; 139 Huang (mstad2255bib17) 2021; 53 De Souza (mstad2255bib43) 2013; 40 Chen (mstad2255bib46) 2018; 178 |
| References_xml | – volume: 19 start-page: 3892 year: 2014 ident: mstad2255bib45 article-title: Research on synchronization of chaotic delayed neural networks with stochastic perturbation using impulsive control method publication-title: Commun. Nonlinear Sci. Numer. Simul. doi: 10.1016/j.cnsns.2013.12.012 – volume: 70 start-page: 1 year: 2020 ident: mstad2255bib15 article-title: A new interpretable learning method for fault diagnosis of rolling bearings publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2020.3043873 – volume: 158 year: 2020 ident: mstad2255bib7 article-title: Trends in audio signal feature extraction methods publication-title: Appl. Acoust. doi: 10.1016/j.apacoust.2019.107020 – start-page: 670 year: 2021 ident: mstad2255bib29 article-title: Power distribution transformer fault diagnosis with unbalanced samples based on neighborhood component analysis and k-nearest neighbors doi: 10.1109/ PSGEC51302.2021.9541829 – start-page: 49 year: 2019 ident: mstad2255bib10 article-title: A vibration signal anomaly detection method based on frequency component clustering and isolated forest algorithm doi: 10.1109/AUTEEE48671.2019.9033363 – volume: 8 start-page: 1627 year: 2021 ident: mstad2255bib19 article-title: A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2021.1004129 – volume: 138 year: 2020 ident: mstad2255bib6 article-title: Applications of machine learning to machine fault diagnosis: a review and roadmap publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.106587 – volume: 25 start-page: 197 year: 2016 ident: mstad2255bib34 article-title: A random forest guided tour publication-title: Test doi: 10.1007/s11749-016-0481-7 – volume: 118 start-page: 1 year: 2020 ident: mstad2255bib14 article-title: Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach publication-title: Int. J. Refrig. doi: 10.1016/j.ijrefrig.2020.06.009 – volume: 29 year: 2022 ident: mstad2255bib2 article-title: Performance assessment of selective machine learning techniques for improved PV array fault diagnosis publication-title: Sustain. Energy Grids Netw. doi: 10.1016/j.segan.2021.100582 – volume: 417 start-page: 36 year: 2020 ident: mstad2255bib5 article-title: A comprehensive review on convolutional neural network in machine fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 13 start-page: 662 year: 2019 ident: mstad2255bib8 article-title: Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS publication-title: IET Electr. Power Appl. doi: 10.1049/iet-epa.2018.5274 – volume: 119 start-page: 152 year: 2022 ident: mstad2255bib3 article-title: Intelligent fault diagnosis of machines with small & imbalanced data: a state-of-the-art review and possible extensions publication-title: ISA Trans. doi: 10.1016/j.isatra.2021.02.042 – volume: 65 start-page: 4973 year: 2017 ident: mstad2255bib4 article-title: LiftingNet: a novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2767540 – volume: 9 start-page: 2579 year: 2008 ident: mstad2255bib16 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 8 start-page: 22 year: 2020 ident: mstad2255bib23 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2019.1708830 – volume: 98 start-page: 517 year: 2019 ident: mstad2255bib39 article-title: A novel approach of analog circuit fault diagnosis utilizing RFT noise estimation publication-title: Analog Integr. Circuits Signal Process. doi: 10.1007/s10470-018-1351-x – volume: vol 915 start-page: 273 year: 2021 ident: mstad2255bib18 article-title: Swarm intelligence: a review of optimization algorithms based on animal behavior doi: 10.1007/978-3-030-58728-4_16 – volume: 39 start-page: 2627 year: 2023 ident: mstad2255bib26 article-title: Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems publication-title: Eng. Comput. doi: 10.1007/s00366-022-01604-x – start-page: 470 year: 2019 ident: mstad2255bib1 article-title: A new method for fault diagnosis of building electrical system based on RBF-BP neural network doi: 10.1109/ ICICAS48597.2019.00104 – year: 2019 ident: mstad2255bib12 article-title: Feature selection and feature extraction in pattern analysis: a literature review – start-page: 537 year: 2020 ident: mstad2255bib25 article-title: An improved sparrow search algorithm doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00093 – volume: 30 start-page: 3847 year: 2018 ident: mstad2255bib42 article-title: A novel image encryption scheme based on orthogonal matrix, skew tent map and XOR operation publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-2970-3 – volume: 33 year: 2022 ident: mstad2255bib37 article-title: Application of multi-kernel relevance vector machine and data pre-processing by complementary ensemble empirical mode decomposition and mutual dimensionless in fault diagnosis publication-title: Meas. Sci. Technol. doi: 10.1088/1361-6501/ac86e5 – volume: vol 4 start-page: 1942 year: 1995 ident: mstad2255bib20 article-title: Particle swarm optimization doi: 10.1109/ICNN.1995.488968 – volume: 220 year: 2021 ident: mstad2255bib24 article-title: A stochastic configuration network based on chaotic sparrow search algorithm publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.106924 – volume: 70 start-page: 87 year: 2016 ident: mstad2255bib48 article-title: Fault diagnosis in spur gears based on genetic algorithm and random forest publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2015.08.030 – volume: 16 start-page: 54 year: 2019 ident: mstad2255bib47 article-title: Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2019.2915559 – volume: 178 start-page: 250 year: 2018 ident: mstad2255bib46 article-title: Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents publication-title: Energy Convers. Manage. doi: 10.1016/j.enconman.2018.10.040 – volume: 45 start-page: 5 year: 2001 ident: mstad2255bib32 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 66 start-page: 284 year: 2017 ident: mstad2255bib11 article-title: A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery publication-title: ISA Trans. doi: 10.1016/j.isatra.2016.10.014 – volume: 139 year: 2020 ident: mstad2255bib36 article-title: A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.106609 – volume: 32 start-page: 10773 year: 2020 ident: mstad2255bib41 article-title: Intelligent bearing fault diagnosis using PCA–DBN framework publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04612-z – volume: 40 year: 2021 ident: mstad2255bib13 article-title: Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) publication-title: Comput. Sci. Rev. doi: 10.1016/j.cosrev.2021.100378 – volume: 22 start-page: 387 year: 2018 ident: mstad2255bib21 article-title: Particle swarm optimization algorithm: an overview publication-title: Soft Comput. doi: 10.1007/s00500-016-2474-6 – volume: 313 start-page: 47 year: 2018 ident: mstad2255bib9 article-title: A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.05.002 – volume: 8 start-page: 103 year: 2017 ident: mstad2255bib31 article-title: Fault classification in power systems using EMD and SVM publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2015.08.005 – volume: 108 start-page: 33 year: 2018 ident: mstad2255bib27 article-title: Artificial intelligence for fault diagnosis of rotating machinery: a review publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2018.02.016 – volume: 69 start-page: 46 year: 2014 ident: mstad2255bib22 article-title: Grey wolf optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – volume: vol 937 start-page: 99 year: 2020 ident: mstad2255bib30 article-title: Supervised classification algorithms in machine learning: a survey and review doi: 10.1007/ 978-981-13-7403-6_11 – volume: 13 start-page: 4567 year: 2023 ident: mstad2255bib38 article-title: A fault diagnosis method for building electrical systems based on the combination of variational modal decomposition and new mutual dimensionless publication-title: Sci. Rep. doi: 10.1038/s41598-022-27031-y – volume: 17 start-page: 5581 year: 2017 ident: mstad2255bib35 article-title: Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2017.2726011 – volume: 100 start-page: 181 year: 2019 ident: mstad2255bib40 article-title: Analog circuit soft fault diagnosis utilizing matrix perturbation analysis publication-title: Analog Integr. Circuits Signal Process. doi: 10.1007/s10470-019-01433-x – volume: 8 start-page: 341 year: 2020 ident: mstad2255bib33 article-title: Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: a review publication-title: J. Data Anal. Inf. Process. doi: 10.4236/jdaip.2020.84020 – volume: 4 start-page: 1883 year: 2009 ident: mstad2255bib28 article-title: K-nearest neighbor publication-title: Scholarpedia doi: 10.4249/scholarpedia.1883 – volume: 17 start-page: 436 year: 2012 ident: mstad2255bib44 article-title: A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2012.2196047 – volume: 53 start-page: 443 year: 2021 ident: mstad2255bib17 article-title: Wavelet packet decomposition-based multiscale CNN for fault diagnosis of wind turbine gearbox publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2021.3123667 – volume: 40 start-page: 4887 year: 2013 ident: mstad2255bib43 article-title: Search based constrained test case selection using execution effort publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.02.018 – volume: 17 start-page: 31 year: 2001 ident: mstad2255bib49 article-title: Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases publication-title: IEEE Electr. Insul. Mag. doi: 10.1109/57.917529 |
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