{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T02:06:40Z","timestamp":1781230000043,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T00:00:00Z","timestamp":1594857600000},"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>Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models\u2019 structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.<\/jats:p>","DOI":"10.3390\/s20143949","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T10:54:46Z","timestamp":1594896886000},"page":"3949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5841-0561","authenticated-orcid":false,"given":"Francisco","family":"Arellano-Espitia","sequence":"first","affiliation":[{"name":"MCIA Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-838X","authenticated-orcid":false,"given":"Miguel","family":"Delgado-Prieto","sequence":"additional","affiliation":[{"name":"MCIA Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2916-8395","authenticated-orcid":false,"given":"Victor","family":"Martinez-Viol","sequence":"additional","affiliation":[{"name":"MCIA Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9026-6694","authenticated-orcid":false,"given":"Juan Jose","family":"Saucedo-Dorantes","sequence":"additional","affiliation":[{"name":"HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0868-2918","authenticated-orcid":false,"given":"Roque Alfredo","family":"Osornio-Rios","sequence":"additional","affiliation":[{"name":"HSPdigital CA-Mecatronica Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,16]]},"reference":[{"key":"ref_1","first-page":"125","article-title":"Data analytics framework for Industry 4.0: Enabling collaboration for added benefits","volume":"117","author":"Mohamed","year":"2019","journal-title":"IET Collab. Int. Manuf."},{"key":"ref_2","first-page":"47","article-title":"Walking the Torque: Proposed Work Plan for Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems","volume":"2011","author":"Falkner","year":"2011","journal-title":"IEA Energy Pap."},{"key":"ref_3","first-page":"1","article-title":"High Power Density PMSM with Lightweight Structure and High-Performance Soft Magnetic Alloy Core","volume":"29","author":"Fang","year":"2019","journal-title":"IEEE Trans. Appl. Supercond."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMAG.2018.2832076","article-title":"Rotor Design Optimization of a New Flux-Assisted Consequent Pole Spoke-Type Permanent Magnet Torque Motor for Low-Speed Applications","volume":"54","author":"Onsal","year":"2018","journal-title":"IEEE Trans. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","article-title":"Big Data Deep Learning: Challenges and Perspectives","volume":"2","author":"Chen","year":"2014","journal-title":"IEEE Access."},{"key":"ref_6","first-page":"4415","article-title":"Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique","volume":"2019","author":"Barusu","year":"2019","journal-title":"J. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1109\/TIA.2018.2805840","article-title":"Electrical Monitoring of Mechanical Defects in Induction Motor-Driven V-Belt\u2013Pulley Speed Reduction Couplings","volume":"54","author":"Kang","year":"2018","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ymssp.2017.03.016","article-title":"Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms","volume":"94","author":"Gangsar","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","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":"Bram","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3398","DOI":"10.1109\/TIE.2012.2219838","article-title":"Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks","volume":"60","author":"Prieto","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1109\/TIE.2010.2051398","article-title":"The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors","volume":"58","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TII.2012.2221131","article-title":"Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT","volume":"9","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4433","DOI":"10.1109\/TIE.2019.2924607","article-title":"Analytical Modeling of Misalignment in Axial Flux Permanent Magnet Machine","volume":"67","author":"Guo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"66","DOI":"10.23919\/CJEE.2018.8471291","article-title":"Thorough validation of a rotor fault diagnosis methodology in laboratory and field soft-started induction motors","volume":"4","year":"2018","journal-title":"Chin. J. Electr. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TIM.2019.2890933","article-title":"Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index","volume":"69","author":"Huang","year":"2019","journal-title":"IEEE Trans. Instr. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Merizalde, Y., Hernandez-Callejo, L., and Duque-Perez, O. (2017). State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors. Energies, 10.","DOI":"10.3390\/en10071056"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/TEC.2014.2341620","article-title":"Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals","volume":"30","author":"Harmouche","year":"2015","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jing, L., Wang, T., Zhao, M., and Wang, P. (2017). An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors, 17.","DOI":"10.3390\/s17020414"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1109\/TIE.2015.2416673","article-title":"Rotor speed-based bearing fault diagnosis (RSB-BFD) under variable speed and constant load","volume":"62","author":"Hamadache","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TMECH.2013.2260865","article-title":"Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors","volume":"19","author":"Esfahani","year":"2014","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TIM.2017.2759418","article-title":"Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning","volume":"67","author":"Sun","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TSMC.2017.2697842","article-title":"Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components","volume":"48","author":"Deutsch","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.isatra.2017.03.017","article-title":"Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet","volume":"69","author":"Shao","year":"2017","journal-title":"ISA Trans."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/TSM.2018.2825482","article-title":"Deep-Structured Machine Learning Model for the Recognition of Mixed-Defect Patterns in Semiconductor Fabrication Processes","volume":"31","author":"Tello","year":"2018","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.measurement.2016.04.007","article-title":"A sparse auto-encoder-based deep neural network approach for induction motor faults classification","volume":"89","author":"Sun","year":"2016","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.sigpro.2016.07.028","article-title":"Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification","volume":"130","author":"Lu","year":"2017","journal-title":"Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yuan, M., Wu, Y., and Lin, L. (2016, January 10\u201312). Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. Proceedings of the 2016 IEEE International Conference on Aircraft Utility Systems (AUS), Piscataway, NJ, USA.","DOI":"10.1109\/AUS.2016.7748035"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.jmsy.2017.04.012","article-title":"Virtualization and deep recognition for System Fault Classification","volume":"44","author":"Wang","year":"2017","journal-title":"J. Manuf. Syst."},{"key":"ref_30","first-page":"1347","article-title":"A deep learning approach for fault diagnosis of induction motors in manufacturing, Chinese","volume":"30","author":"Shao","year":"2017","journal-title":"J. Mech. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1687814017743113","DOI":"10.1177\/1687814017743113","article-title":"A new fault diagnosis method based on deep belief network and support vector machine with Teager-Kaiser energy operator for bearings","volume":"9","author":"Han","year":"2017","journal-title":"Adv. Mech. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6084","DOI":"10.1109\/ACCESS.2018.2889093","article-title":"Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_35","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Navarro, L., Delgado, M., Urresty, J., Cusid\u00f3, J., and Romeral, L. (2010, January 3\u20136). Condition monitoring system for characterization of electric motor ball bearings with distributed fault using fuzzy inference tools. Proceedings of the 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, Austin, TX, USA.","DOI":"10.1109\/IMTC.2010.5488092"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kia, S.H., Henao, H., and Capolino, G. (2012, January 2\u20135). A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis. Proceedings of the 2012 XXth International Conference on Electrical Machines, Marseille, France.","DOI":"10.1109\/ICElMach.2012.6350079"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.ymssp.2017.02.037","article-title":"Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis","volume":"94","author":"Zhang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TIA.2015.2508423","article-title":"Reliable Detection of Rotor Faults Under the Influence of Low-Frequency Load Torque Oscillations for Applications with Speed Reduction Couplings","volume":"52","author":"Kim","year":"2015","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1098\/rsif.2005.0058","article-title":"The local mean decomposition and its application to EED perception data","volume":"2","author":"Smith","year":"2005","journal-title":"J. R. Soc. Interface"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_43","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2000). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s00170-013-4797-0","article-title":"Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks","volume":"68","author":"Zhang","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.ymssp.2004.10.010","article-title":"Application of Dempster\u2013Shafer theory in fault diagnosis of induction motors using vibration and current signals","volume":"20","author":"Yang","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TMECH.2019.2928967","article-title":"Multilevel Information Fusion for Induction Motor Fault Diagnosis","volume":"24","author":"Wang","year":"2019","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8775","DOI":"10.1109\/TPEL.2017.2776939","article-title":"A novel open-circuit fault diagnosis method for voltage source inverters with a single current sensor","volume":"33","author":"Yan","year":"2018","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_48","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-Dimensional Feature Reduction","volume":"53","author":"Saucedo","year":"2017","journal-title":"IEEE Trans. Ind. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3949\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:57Z","timestamp":1760176137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,16]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143949"],"URL":"https:\/\/doi.org\/10.3390\/s20143949","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,16]]}}}