{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:58:31Z","timestamp":1765357111424,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time\u2013frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.<\/jats:p>","DOI":"10.3390\/e24050614","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T12:06:01Z","timestamp":1651147561000},"page":"614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot Pattern"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3987-2263","authenticated-orcid":false,"given":"Liangyuan","family":"Huang","sequence":"first","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Jihong","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3430-557X","authenticated-orcid":false,"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Guoji","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.ymssp.2016.06.032","article-title":"Methodology for fault detection in induction motors via sound and vibration signals","volume":"83","year":"2017","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.infrared.2017.10.007","article-title":"Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor","volume":"87","author":"Singh","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1109\/TIA.2003.816531","article-title":"A frequency-domain detection of stator winding faults in induction machines using an external flux sensor","volume":"39","author":"Henao","year":"2003","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TIM.2019.2901514","article-title":"A Concentrated Time\u2013Frequency Analysis Tool for Bearing Fault Diagnosis","volume":"69","author":"Yu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cui, H., Guan, Y., Chen, H., and Deng, W. (2021). A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection. Appl. Sci., 11.","DOI":"10.3390\/app11125385"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108145","DOI":"10.1016\/j.ymssp.2021.108145","article-title":"Enhancement of time-frequency post-processing readability for nonstationary signal analysis of rotating machinery: Principle and validation","volume":"163","author":"Zhang","year":"2022","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8475","DOI":"10.1007\/s13369-021-05527-5","article-title":"Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features","volume":"46","author":"Sikder","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","article-title":"Generative adversarial networks for data augmentation in machine fault diagnosis","volume":"106","author":"Shao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nishat Toma, R., Kim, C.-H., and Kim, J.-M. (2021). Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network. Electronics, 10.","DOI":"10.3390\/electronics10111248"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106908","DOI":"10.1016\/j.ymssp.2020.106908","article-title":"Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review","volume":"144","author":"Gangsar","year":"2020","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/63.737588","article-title":"Induction motors\u2019 faults detection and localization using stator current advanced signal processing techniques","volume":"14","author":"Benbouzid","year":"1999","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/TIE.2008.917108","article-title":"Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring","volume":"55","author":"Blodt","year":"2008","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ymssp.2017.12.010","article-title":"The reflection of evolving bearing faults in the stator current\u2019s extended park vector approach for induction machines","volume":"107","author":"Corne","year":"2018","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1007\/s00202-020-01000-y","article-title":"Temporal envelope detection by the square root of the three-phase currents for IM rotor fault diagnosis","volume":"102","author":"Khelfi","year":"2020","journal-title":"Electr. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.isatra.2018.07.020","article-title":"Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor","volume":"80","author":"Abdelsalam","year":"2018","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/TEC.2019.2951008","article-title":"Diagnosis of Rotor Asymmetries Faults in Induction Machines Using the Rectified Stator Current","volume":"35","year":"2020","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, Z., Zhen, D., Gu, F., and Ball, A. (2019). Modulation Sideband Separation Using the Teager\u2013Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors. Energies, 12.","DOI":"10.3390\/en12234437"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Areias, I.A., Borges da Silva, L.E., Bonaldi, E.L., de Lacerda de Oliveira, L.E., Lambert-Torres, G., and Bernardes, V.A. (2019). Evaluation of Current Signature in Bearing Defects by envelope analysis of the vibration in induction motors. Energies, 12.","DOI":"10.3390\/en12214029"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1109\/TIM.2019.2900143","article-title":"An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks","volume":"68","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1109\/TIM.2019.2956332","article-title":"A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107817","DOI":"10.1016\/j.ymssp.2021.107817","article-title":"Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image","volume":"159","author":"Sun","year":"2021","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21798","DOI":"10.1109\/JSEN.2021.3102019","article-title":"Motor Fault Diagnosis Using Image Visual Information and Bag of Words Model","volume":"21","author":"Long","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1121\/1.393918","article-title":"On the use of symmetrized dot patterns for the visual characterization of speech waveforms and other sampled data","volume":"80","author":"Pickover","year":"1986","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1002\/(SICI)1097-0010(199710)75:2<173::AID-JSFA858>3.0.CO;2-9","article-title":"Effect of lag on the symmetrised dot pattern (SDP) displays of the mechanical signatures of crunchy cereal foods","volume":"75","author":"DeRosier","year":"1997","journal-title":"J. Sci. Food Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1214\/aoms\/1177728190","article-title":"Remarks on Some Nonparametric Estimates of a Density Function","volume":"27","author":"Rosenblatt","year":"1956","journal-title":"Ann. Math. Stat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","article-title":"On Estimation of a Probability Density Function and Mode","volume":"33","author":"Parzen","year":"1962","journal-title":"Ann. Math. Stat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1109\/TIE.2014.2370936","article-title":"Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback\u2013Leibler Divergence Based on Stator Current Measurements","volume":"62","author":"Giantomassi","year":"2015","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/5\/614\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:02:32Z","timestamp":1760137352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/5\/614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["e24050614"],"URL":"https:\/\/doi.org\/10.3390\/e24050614","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}