{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:06:05Z","timestamp":1775066765275,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection.<\/jats:p>","DOI":"10.3390\/sym13010104","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN"],"prefix":"10.3390","volume":"13","author":[{"given":"Chun-Yao","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Yi","family":"Ou","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.energy.2016.08.039","article-title":"Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery","volume":"136","author":"Brkovic","year":"2017","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1109\/TIA.2015.2411662","article-title":"Tolerant control for power transistor faults in switched reluctance motor drives","volume":"51","author":"Ro","year":"2015","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.ymssp.2011.10.018","article-title":"CNC machine tool\u2019s wear diagnostic and prognostic by using dynamic Bayesian networks","volume":"28","author":"Medjaher","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"76993","DOI":"10.1109\/ACCESS.2018.2883988","article-title":"Induction motor failure analysis: An automatic methodology based on infrared imaging","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hsueh, Y.-M., Ittangihal, V.R., Wu, W.-B., Chang, H.-C., and Kuo, C.-C. (2019). Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. Symmetry, 11.","DOI":"10.3390\/sym11101212"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Korkua1, S., Jain, H., Lee, W.-J., and Kwan, C. (2010, January 9\u201313). Wireless Health Monitoring System for Vibration Detection of Induction Motors. Proceedings of the 2010 IEEE Industrial and Commercial Power Systems Technical Conference, Tallahassee, FL, USA.","DOI":"10.1109\/ICPS.2010.5489899"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ge, M., Wand, J., Xu, Y., Zhang, F., Bai, K., and Ren, X. (2018). Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification. Symmetry, 10.","DOI":"10.3390\/sym10120730"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TIM.2015.2498978","article-title":"Feature Knowledge Based Fault Detection of Induction Motors through the Analysis of Stator Current Data","volume":"65","author":"Yang","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Oyamada, M. (2019, January 9\u201312). Extracting feature engineering knowledge from data science notebooks. Proceedings of the IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006522"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/TIE.2014.2327589","article-title":"Heterogeneous feature models and feature selection applied to bearing fault diagnosis","volume":"62","author":"Rauber","year":"2014","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gligorijevic, J., Gajic, D., Brkovic, A., Savic Gajic, I., Georgieva, O., and Gennaro, S.-D. (2016). Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry. Sensors, 16.","DOI":"10.3390\/s16030316"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/TGRS.2015.2466660","article-title":"Synchrosqueezing S-transform and its application in seismic spectral decomposition","volume":"54","author":"Huang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Zhang, S., and Huang, D. (2013, January 25\u201327). EEG time-frequency analysis based on the improved S-transform. Proceedings of the 25th Chinese Control and Decision Conference, Guiyang, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.ymssp.2014.07.009","article-title":"Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions","volume":"52","author":"Feng","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.acha.2013.07.003","article-title":"Using synchrosqueezing transform to discover breathing dynamics from ECG signals","volume":"36","author":"Wu","year":"2014","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6016","DOI":"10.1016\/j.jsv.2013.04.026","article-title":"A synchrosqueezed wavelet transform enhanced by extended analytical mode decomposition method for dynamic signal reconstruction","volume":"332","author":"Wang","year":"2013","journal-title":"J. Sound Vib."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.sigpro.2015.01.022","article-title":"Nonlinear squeezing time-frequency transform for weak signal detection","volume":"113","author":"Wang","year":"2015","journal-title":"Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.sigpro.2014.08.010","article-title":"Synchrosqueezing-based time-frequency analysis of multivariate data","volume":"106","author":"Ahrabian","year":"2015","journal-title":"Signal Process."},{"key":"ref_19","unstructured":"Kwan, C., Zhang, X., Xu, R., and Haynes, L. (2003, January 14\u201319). A novel approach to fault diagnostics and prognostics. Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1109\/TGRS.2008.2001386","article-title":"A Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and Display","volume":"46","author":"Yang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Wang, Z. (2017, January 29\u201331). A hybrid model for blood pressure prediction from a PPG signal based on MIV and GA-BP neural network. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Guilin, China.","DOI":"10.1109\/FSKD.2017.8393073"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1109\/TPWRD.2011.2149547","article-title":"Optimal feature selection for power-quality disturbances classification","volume":"26","author":"Lee","year":"2006","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qian, T., Li, X., Ayhan, B., Xu, R., Kwan, C., and Griffin, T. (2006). Application of Support Vector Machines to Vapor Detection and Classification for Environmental Monitoring of Spacecraft. ISNN 2006. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/11760191_177"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/LGRS.2017.2778045","article-title":"Time-Frequency Analysis of Seismic Data Using a Three Parameters S Transform","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TSP.2009.2028972","article-title":"A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous S-transform spectrum nonredundantly","volume":"58","author":"Brown","year":"2010","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSYST.2013.2286539","article-title":"Mass classification in mammograms using selected geometry and texture features and a new SVM-based feature selection method","volume":"8","author":"Liu","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/TEVC.2010.2053935","article-title":"Stochastic Stability Analysis of the Linear Continuous and Discrete PSO Models","volume":"15","year":"2011","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:44Z","timestamp":1760159324000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/1\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["sym13010104"],"URL":"https:\/\/doi.org\/10.3390\/sym13010104","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,8]]}}}