{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T20:13:09Z","timestamp":1781122389222,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"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>Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback\u2013Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.<\/jats:p>","DOI":"10.3390\/s22020586","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T03:14:03Z","timestamp":1642130043000},"page":"586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2935-1819","authenticated-orcid":false,"given":"Alberto","family":"Gasc\u00f3n","sequence":"first","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5316-8171","authenticated-orcid":false,"given":"Roberto","family":"Casas","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3431-5863","authenticated-orcid":false,"given":"David","family":"Buldain","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-7840","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Marco","sequence":"additional","affiliation":[{"name":"Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain"},{"name":"GeoSpatium Lab S.L., Carlos Marx 6, 50015 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"ref_1","unstructured":"(2021, December 03). Why Predictive Maintenance Is Driving Industry 4.0. Available online: https:\/\/files.solidworks.com\/partners\/pdfs\/why-predictive-maintenance-is-driving-industry-4.0405.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/TSMCC.2013.2257752","article-title":"Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals","volume":"44","author":"Henriquez","year":"2014","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s11831-018-9286-z","article-title":"Condition Monitoring and Fault Diagnosis of Induction Motors: A Review","volume":"26","author":"Choudhary","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_4","first-page":"65","article-title":"Metodolog\u00eda de Monitoreo, Detecci\u00f3n y Diagn\u00f3stico de Fallos En Motores As\u00edncronos de Inducci\u00f3n","volume":"5","author":"Castelli","year":"2007","journal-title":"Mem. Investig. Ing."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mardacany, E. (2014, January 4\u20135). Smart cities characteristics: Importance of buit environments components. Proceedings of the IET Conference on Future Intelligent Cities, London, UK.","DOI":"10.1049\/ic.2014.0045"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Purnachand, K., Shabbeer, M., Rao, P.N.V.S.M., and Babu, C.M. (2021, January 18\u201319). Predictive maintenance of machines and industrial equipment. Proceedings of the 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India.","DOI":"10.1109\/CSNT51715.2021.9509696"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114598","DOI":"10.1016\/j.eswa.2021.114598","article-title":"Predictive Maintenance System for Production Lines in Manufacturing: A Machine Learning Approach Using IoT Data in Real-Time","volume":"173","author":"Ayvaz","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"45797","DOI":"10.1109\/ACCESS.2020.2977667","article-title":"Integrated Intelligent Green Scheduling of Predictive Maintenance for Complex Equipment Based on Information Services","volume":"8","author":"Mi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nangia, S., Makkar, S., and Hassan, R. (2020). IoT Based Predictive Maintenance in Manufacturing Sector. SSRN Electron. J., 1\u20137.","DOI":"10.2139\/ssrn.3563559"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103298","DOI":"10.1016\/j.compind.2020.103298","article-title":"Machine Learning and Reasoning for Predictive Maintenance in Industry 4.0: Current Status and Challenges","volume":"123","author":"Dalzochio","year":"2020","journal-title":"Comput. Ind."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.ymssp.2013.02.016","article-title":"Application of Power Spectrum, Cepstrum, Higher Order Spectrum and Neural Network Analyses for Induction Motor Fault Diagnosis","volume":"39","author":"Liang","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.7763\/IJMLC.2012.V2.93","article-title":"Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery","volume":"2","author":"Bendjama","year":"2012","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121033","DOI":"10.1109\/ACCESS.2020.3006788","article-title":"An Effective Predictive Maintenance Framework for Conveyor Motors Using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment","volume":"8","author":"Kiangala","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Strauss, P., Schmitz, M., Wostmann, R., and Deuse, J. (2018, January 10\u201313). Enabling of predictive maintenance in the brownfield through low-cost Sensors, an IIoT-architecture and machine learning. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622076"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saponara, S., Fanucci, L., Bernardo, F., and Falciani, A. (2015, January 15\u201317). A Network of Vibration Measuring Nodes with Integrated Signal Processing for Predictive Maintenance of High Power Transformers. Proceedings of the 2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP), Sienna, Italy.","DOI":"10.1109\/WISP.2015.7139154"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pereira, R.R., Diniz da Silva, V.A., Brito, J.N., and Daniel Nolasco, J. (2016, January 13\u201315). On-line monitoring induction motors by fuzzy logic: A study for predictive maintenance operators. Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603373"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Novoa, C.G., Berr\u00edos, G.A.G., and S\u00f6derberg, R.A. (2017, January 20\u201322). Predictive maintenance for motors based on vibration analysis with compact rio. Proceedings of the 2017 IEEE Central America and Panama Student Conference (CONESCAPAN), Panama City, Panama.","DOI":"10.1109\/CONESCAPAN.2017.8277603"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"178891","DOI":"10.1109\/ACCESS.2019.2953019","article-title":"Predictive Maintenance of Induction Motors Using Ultra-Low Power Wireless Sensors and Compressed Recurrent Neural Networks","volume":"7","author":"Markiewicz","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Patil, S.S., and Gaikwad, J.A. (2013, January 4\u20136). Vibration analysis of electrical rotating machines using FFT: A method of predictive maintenance. Proceedings of the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India.","DOI":"10.1109\/ICCCNT.2013.6726711"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kavana, V., and Neethi, M. (2018, January 14\u201315). Fault analysis and predictive maintenance of induction motor using machine learning. Proceedings of the 2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Msyuru, India.","DOI":"10.1109\/ICEECCOT43722.2018.9001543"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rosli, N.S.B., Ibrahim, R.B., and Ismail, I. (2019, January 20\u201323). Optimized neural network of predictive maintenance for Air Booster Compressor (ABC) motor failure. Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand.","DOI":"10.1109\/I2MTC.2019.8827145"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1049\/iet-epa.2015.0469","article-title":"Application of Intelligent Tools to Detect and Classify Broken Rotor Bars in Three-phase Induction Motors Fed by an Inverter","volume":"10","author":"Godoy","year":"2016","journal-title":"IET Electr. Power Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.measurement.2012.11.011","article-title":"Exploiting Sound Signals for Fault Diagnosis of Bearings Using Decision Tree","volume":"46","author":"Amarnath","year":"2013","journal-title":"Measurement"},{"key":"ref_24","first-page":"1804","article-title":"An Overview on Vibration Analysis Techniques for the Diagnosis of Rolling Element Bearing Faults","volume":"4","author":"Patidar","year":"2013","journal-title":"Int. J. Eng. Trends Technol. IJETT"},{"key":"ref_25","unstructured":"(2021, December 01). My NTi Audio. Available online: https:\/\/www.nti-audio.com\/es\/servicio\/conocimientos\/transformacion-rapida-de-fourier-fft."},{"key":"ref_26","unstructured":"Vilela, R.M., Metrglho, J.C., and Cardoso, J.C. (2004, January 12\u201315). Machine and industrial monitorization system by analysis of acoustic signatures. Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No. 04CH37521), Dubrovnik, Croatia."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1007\/s10033-017-0150-0","article-title":"Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis","volume":"30","author":"Zhang","year":"2017","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/586\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:14:52Z","timestamp":1760364892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/586"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,13]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020586"],"URL":"https:\/\/doi.org\/10.3390\/s22020586","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,13]]}}}