112062 20240319080953.0 doi 10.3390/s22020586 sideral 127730 ART-2022-127730 eng Gascón, Alberto Universidad de Zaragoza (orcid)0000-0003-2935-1819 Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City 2022 Access copy available to the general public Unrestricted 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–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. info:eu-repo/grantAgreement/ES/DGA/T59-20R info:eu-repo/grantAgreement/ES/MINECO/PTQ-17-09481 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.9 2022 CHEMISTRY, ANALYTICAL 26 / 86 = 0.302 2022 Q2 T1 INSTRUMENTS & INSTRUMENTATION 19 / 63 = 0.302 2022 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 100 / 274 = 0.365 2022 Q2 T2 0.764 2022 Instrumentation 2022 Q1 Analytical Chemistry 2022 Q1 Medicine (miscellaneous) 2022 Q2 Information Systems 2022 Q2 Biochemistry 2022 Q2 Atomic and Molecular Physics, and Optics 2022 Q2 Electrical and Electronic Engineering 2022 Q2 6.8 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Casas, Roberto Universidad de Zaragoza (orcid)0000-0001-5316-8171 Buldain, David Universidad de Zaragoza (orcid)0000-0003-3431-5863 Marco, Álvaro Universidad de Zaragoza (orcid)0000-0002-7396-7840 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 22, 2 (2022), 586 [23 pp.] Sensors Sensors 1424-8220 7015059 http://zaguan.unizar.es/record/112062/files/texto_completo.pdf Versión publicada 2735334 http://zaguan.unizar.es/record/112062/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:112062 articulos driver 2024-03-18-13:20:03 ARTICLE