{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T11:42:59Z","timestamp":1777981379723,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time\u2013frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.<\/jats:p>","DOI":"10.3390\/s20133721","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T06:51:20Z","timestamp":1593759080000},"page":"3721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-1396","authenticated-orcid":false,"given":"Martin","family":"Valtierra-Rodriguez","sequence":"first","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo C.P. 76807, Qro., Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9141-3454","authenticated-orcid":false,"given":"Jesus R.","family":"Rivera-Guillen","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo C.P. 76807, Qro., Mexico"}]},{"given":"Jesus A.","family":"Basurto-Hurtado","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo C.P. 76807, Qro., Mexico"}]},{"given":"J. Jesus","family":"De-Santiago-Perez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo C.P. 76807, Qro., Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6692-5469","authenticated-orcid":false,"given":"David","family":"Granados-Lieberman","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energ\u00eda El\u00e9ctrica, Departamento de Ingenier\u00eda Electromec\u00e1nica, Tecnol\u00f3gico Nacional de Mexico, Instituto Tecnol\u00f3gico Superior de Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, Colonia El Copal, Irapuato, Guanajuato C.P. 36821, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-0220","authenticated-orcid":false,"given":"Juan P.","family":"Amezquita-Sanchez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo C.P. 76807, Qro., Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/41.873206","article-title":"A review of induction motors signature analysis as a medium for faults detection","volume":"47","author":"Benbouzid","year":"2000","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1109\/TEC.2005.847955","article-title":"Condition monitoring and fault diagnosis of electrical motors\u2014A review","volume":"20","author":"Nandi","year":"2005","journal-title":"IEEE Trans. Energy Conver."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/60.9364","article-title":"Noninvasive detection of broken rotor bars in operating induction motors","volume":"3","author":"Kliman","year":"1998","journal-title":"IEEE Trans. Energy Conver."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zamudio-Ram\u00edrez, I., Osornio-R\u00edos, R.A., Antonino-Daviu, J.A., and Quijano-Lopez, A. (2020). Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis. Sensors, 20.","DOI":"10.3390\/s20051477"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.ymssp.2007.11.018","article-title":"A simplified scheme for induction motor condition monitoring","volume":"22","author":"Negrea","year":"2008","journal-title":"Mech. Syst. Signal Pr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.measurement.2012.10.008","article-title":"Motor square current signature analysis for induction motor rotor diagnosis","volume":"46","author":"Pires","year":"2013","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.measurement.2010.03.006","article-title":"Estimation of frequency components in stator current for the detection of broken rotor bars in induction machines","volume":"43","author":"Chen","year":"2010","journal-title":"Measurement"},{"key":"ref_8","first-page":"1746","article-title":"Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art","volume":"62","author":"Capolino","year":"2014","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/TIM.2017.2664520","article-title":"Novel FPGA-based methodology for early broken rotor bar detection and classification through homogeneity estimation","volume":"66","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"257","DOI":"10.2478\/msr-2014-0035","article-title":"Diagnostics of DC and induction motors based on the analysis of acoustic signals","volume":"14","author":"Glowacz","year":"2014","journal-title":"Meas. Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ymssp.2013.12.002","article-title":"Sound based induction motor fault diagnosis using Kohonen self-organizing map","volume":"46","author":"Germen","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.egypro.2015.07.769","article-title":"DWT and Hilbert transform for broken rotor bar fault diagnosis in induction machine at low load","volume":"74","author":"Bessam","year":"2015","journal-title":"Energy Proc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.measurement.2017.05.070","article-title":"Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars","volume":"109","year":"2017","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/TIM.2014.2373513","article-title":"Empirical mode decomposition analysis for broken-bar detection on squirrel cage induction motors","volume":"64","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.neucom.2017.09.069","article-title":"1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data","volume":"275","author":"Abdeljaber","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","unstructured":"Huang, D.S. (1996). Systematic Theory of Neural Networks for Pattern Recognition, Publishing House of Electronic Industry of China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1109\/TIM.2018.2813820","article-title":"Incipient broken rotor bar detection in induction motors using vibration signals and the orthogonal matching pursuit algorithm","volume":"67","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1109\/TIM.2009.2031835","article-title":"Diagnosis of induction motor faults in the fractional Fourier domain","volume":"59","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1109\/TIA.2016.2637307","article-title":"Multifault diagnosis method applied to an electric machine based on high high dimensional feature reduction","volume":"53","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105497","DOI":"10.1016\/j.asoc.2019.105497","article-title":"Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine","volume":"81","year":"2019","journal-title":"Appl. Soft. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pereira, L.A., Fernandes, D., Gazzana, D.S., Libano, F.B., and Haffner, S. (2006, January 15\u201318). Application of the welch, burg and MUSIC methods to the detection of rotor cage faults of induction motors. Proceedings of the IEEE\/PES Transmission & Distribution Conference and Exposition: Latin America, Caracas, Venezuela.","DOI":"10.1109\/TDCLA.2006.311388"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/TIE.2007.896522","article-title":"On the use of a lower sampling rate for broken rotor bar detection with DTFT and AR-based spectrum methods","volume":"55","author":"Ayhan","year":"2008","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"075001","DOI":"10.1088\/1361-6501\/aa6adf","article-title":"Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes","volume":"28","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rezazadeh Mehrjou, M., Mariun, N., Misron, N., Radzi, M.A.M., and Musa, S. (2017). Broken rotor bar detection in LS-PMSM based on startup current analysis using wavelet entropy features. Appl. Sci., 7.","DOI":"10.3390\/app7080845"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Verma, A., and Sarangi, S. (2015). Fault diagnosis of broken rotor bars in induction motor using multiscale entropy and backpropagation neural network. Intelligent Computing and Applications, Springer.","DOI":"10.1007\/978-81-322-2268-2_41"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1109\/TIM.2016.2540941","article-title":"A method for detecting half-broken rotor bar in lightly loaded induction motors using current","volume":"65","author":"Naha","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1109\/TIE.2010.2095391","article-title":"Fault diagnosis in industrial induction machines through discrete wavelet transform","volume":"58","author":"Bouzida","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.isatra.2018.04.019","article-title":"Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive","volume":"79","author":"Ameid","year":"2018","journal-title":"ISA Trans."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1007\/s00202-018-0700-1","article-title":"Broken rotor bar detection using empirical demodulation and wavelet transform: Suitable for industrial application","volume":"100","author":"Baccarini","year":"2018","journal-title":"Elect. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Antonino-Daviu, J., Aviyente, S., Strangas, E.G., Riera-Guasp, M., Roger-Folch, J., and P\u00e9rez, R.B. (2011, January 5\u20138). An EMD-based invariant feature extraction algorithm for rotor bar condition monitoring. Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Bologna, Italy.","DOI":"10.1109\/DEMPED.2011.6063696"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8048","DOI":"10.1109\/ACCESS.2017.2702643","article-title":"A comparison of techniques for fault detection in inverter-fed induction motors in transient regime","volume":"5","year":"2017","journal-title":"IEEE Access."},{"key":"ref_32","first-page":"93","article-title":"Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network","volume":"8","author":"Talib","year":"2016","journal-title":"J. Telecommun. Electron. Comput. Eng."},{"key":"ref_33","first-page":"1","article-title":"Shannon Entropy and-K-Means method for automatic diagnosis of broken rotor bars in induction motors using vibration signals","volume":"2016","year":"2016","journal-title":"Shock. Vib."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8681","DOI":"10.1016\/j.eswa.2012.01.214","article-title":"Support vector machine classifier for diagnosis in electrical machines: Application to broken bar","volume":"39","author":"Kamenko","year":"2012","journal-title":"Expert. Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1016\/j.eswa.2007.12.010","article-title":"Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference","volume":"36","author":"Yang","year":"2009","journal-title":"Expert. Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1049\/ip-vis:20045034","article-title":"Properties determining choice of mother wavelet","volume":"152","author":"Ahuja","year":"2005","journal-title":"IEEE Proc. Vis. Image Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2014.12.015","article-title":"Time\u2013frequency features for pattern recognition using high-resolution TFDs: A tutorial review","volume":"40","author":"Boashash","year":"2015","journal-title":"Digit. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1002\/cta.684","article-title":"Real-valued MUSIC algorithm for power harmonics and interharmonics estimation","volume":"39","author":"Cai","year":"2011","journal-title":"Int. J. Circuit Theory Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","article-title":"A review of Convolutional-Neural-Network-based action recognition","volume":"118","author":"Yao","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-09544-z","article-title":"Personalized monitoring and advance warning system for cardiac arrhythmias","volume":"7","author":"Kiranyaz","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1111\/mice.12497","article-title":"Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network","volume":"35","author":"Deng","year":"2020","journal-title":"Comput. Aided. Civ. Inf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cag.2017.10.007","article-title":"Toward real-time 3D object recognition: A lightweight volumetric CNN framework using multitask learning","volume":"71","author":"Zhi","year":"2018","journal-title":"Comput Graph."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","article-title":"Highly accurate machine fault diagnosis using deep transfer learning","volume":"15","author":"Shao","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cogsys.2018.03.002","article-title":"Rolling element bearing fault diagnosis using convolutional neural network and vibration image","volume":"53","author":"Hoang","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A new convolutional neural network-based data-driven fault diagnosis method","volume":"65","author":"Wen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compind.2018.12.013","article-title":"A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals","volume":"105","author":"Wang","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s10033-017-0190-5","article-title":"Motor fault diagnosis based on short-time Fourier transform and convolutional neural network","volume":"30","author":"Wang","year":"2017","journal-title":"Chin. J. Mech. Eng. En."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Li, F.F. (2014, January 23\u201328). Large-scale video classification with convolutional neural networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.epsr.2014.11.021","article-title":"Analysis of fault signatures for the diagnosis of induction motors fed by voltage source inverters using ANOVA and additive models","volume":"121","author":"Gardel","year":"2015","journal-title":"Electr. Power Syst. Res."},{"key":"ref_50","first-page":"1291","article-title":"Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors","volume":"13","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TII.2012.2198659","article-title":"Scale invariant feature extraction algorithm for the automatic diagnosis of rotor asymmetries in induction motors","volume":"9","author":"Aviyente","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.measurement.2018.04.039","article-title":"Enhanced FFT-based method for incipient broken rotor bar detection in induction motors during the startup transient","volume":"124","year":"2018","journal-title":"Measurement"},{"key":"ref_53","unstructured":"Proakis, J., and Manolakis, D. (1996). Digital Signal Processing: Principle, Algorithm, and Applications, Prentice-Hall. [3rd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tan, L., and Jiang, J. (2013). Infinite Impulse Response Filter Design. Digital Signal Processing, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-415893-1.00008-1"},{"key":"ref_55","unstructured":"Nussbaumer, H.J. (2000). Fast Fourier Transform and Convolution Algorithms, Springer Science & Business Media."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.epsr.2013.01.011","article-title":"FPGA-based neural network harmonic estimation for continuous monitoring of the power line in industrial applications","volume":"98","year":"2013","journal-title":"Electr. Power Syst. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1109\/78.869059","article-title":"Improved instantaneous frequency estimation using an adaptive short-time Fourier transform","volume":"48","author":"Kwok","year":"2000","journal-title":"IEEE Trans. Signal Proces."},{"key":"ref_58","first-page":"429","article-title":"Theory of communication","volume":"93","author":"Gabor","year":"1946","journal-title":"IEEE J. Inst. Electr. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, T., Xu, H., Ragulskis, M., Cao, M., and Ostachowicz, W. (2020). A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure. Sensors, 20.","DOI":"10.3390\/s20041059"},{"key":"ref_60","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neucom.2018.09.071","article-title":"A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings","volume":"323","author":"Ieracitano","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.neunet.2020.01.027","article-title":"A deep CNN approach to decode motor preparation of upper limbs from time\u2013frequency maps of EEG signals at source level","volume":"124","author":"Mammone","year":"2020","journal-title":"Neural Netw."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Scherer, D., M\u00fcller, A., and Behnke, S. (2010, January 15\u201318). Evaluation of pooling operations in convolutional architectures for object recognition. Proceedings of the International Conference on Artificial Neural Networks, Thessaloniki, Greece.","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11668-016-0080-7","article-title":"A comparative study of various methods of bearing faults diagnosis using the case Western Reserve University data","volume":"16","author":"Boudiaf","year":"2016","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/TR.2019.2896240","article-title":"Entropy based fault classification using the Case Western Reserve University data: A benchmark study","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Trans. Reliab."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer learning with neural networks for bearing fault diagnosis in changing working conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"168828","DOI":"10.1109\/ACCESS.2019.2954704","article-title":"Demodulation band optimization in envelope analysis for fault diagnosis of rolling element bearings using a real-coded genetic algorithm","volume":"7","author":"Kannan","year":"2019","journal-title":"IEEE Access."},{"key":"ref_68","unstructured":"Zhang, S., Ye, F., Wang, B., and Habetler, T.G. (2019). Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders. arXiv."},{"key":"ref_69","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, The MIT Press."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.measurement.2016.05.010","article-title":"Synchrosqueezing transform-based methodology for broken rotor bars detection in induction motors","volume":"90","year":"2016","journal-title":"Measurement"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.ymssp.2017.02.014","article-title":"Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform","volume":"93","author":"Abdelsalam","year":"2017","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:46:45Z","timestamp":1760176005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,3]]},"references-count":71,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20133721"],"URL":"https:\/\/doi.org\/10.3390\/s20133721","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,3]]}}}