000112062 001__ 112062
000112062 005__ 20240319080953.0
000112062 0247_ $$2doi$$a10.3390/s22020586
000112062 0248_ $$2sideral$$a127730
000112062 037__ $$aART-2022-127730
000112062 041__ $$aeng
000112062 100__ $$0(orcid)0000-0003-2935-1819$$aGascón, Alberto$$uUniversidad de Zaragoza
000112062 245__ $$aProviding Fault Detection from Sensor Data in Complex Machines That Build the Smart City
000112062 260__ $$c2022
000112062 5060_ $$aAccess copy available to the general public$$fUnrestricted
000112062 5203_ $$aHousehold 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–Liebler 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.
000112062 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T59-20R$$9info:eu-repo/grantAgreement/ES/MINECO/PTQ-17-09481
000112062 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000112062 590__ $$a3.9$$b2022
000112062 592__ $$a0.764$$b2022
000112062 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1
000112062 593__ $$aInstrumentation$$c2022$$dQ1
000112062 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1
000112062 593__ $$aAnalytical Chemistry$$c2022$$dQ1
000112062 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000112062 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000112062 593__ $$aInformation Systems$$c2022$$dQ2
000112062 593__ $$aBiochemistry$$c2022$$dQ2
000112062 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2
000112062 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2
000112062 594__ $$a6.8$$b2022
000112062 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000112062 700__ $$0(orcid)0000-0001-5316-8171$$aCasas, Roberto$$uUniversidad de Zaragoza
000112062 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain, David$$uUniversidad de Zaragoza
000112062 700__ $$0(orcid)0000-0002-7396-7840$$aMarco, Álvaro$$uUniversidad de Zaragoza
000112062 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000112062 773__ $$g22, 2 (2022), 586 [23 pp.]$$pSensors$$tSensors$$x1424-8220
000112062 8564_ $$s7015059$$uhttps://zaguan.unizar.es/record/112062/files/texto_completo.pdf$$yVersión publicada
000112062 8564_ $$s2735334$$uhttps://zaguan.unizar.es/record/112062/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000112062 909CO $$ooai:zaguan.unizar.es:112062$$particulos$$pdriver
000112062 951__ $$a2024-03-18-13:20:03
000112062 980__ $$aARTICLE