{"id":4906,"date":"2023-10-19T14:26:31","date_gmt":"2023-10-19T12:26:31","guid":{"rendered":"https:\/\/howlab-dev.i3a.es\/?p=4906"},"modified":"2023-10-19T14:29:04","modified_gmt":"2023-10-19T12:29:04","slug":"alberto-gascon-roche","status":"publish","type":"post","link":"https:\/\/howlab.i3a.es\/es\/alberto-gascon-roche\/","title":{"rendered":"Alberto Gasc\u00f3n Roche"},"content":{"rendered":"
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    Investigadores<\/a><\/h3>\n

    Alberto Gasc\u00f3n Roche<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
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      Research Staff, PhD Student<\/em><\/p>\n

      Tel\u00e9fono:<\/strong> +34 876 555 457<\/p>\n

      Email:<\/strong> algaroche@unizar.es<\/p>\n

      Address:<\/strong> Lab. 6.2.04, Edificio I+D+i, Campus R\u00edo Ebro, C\/ Mariano Esquillor s\/n 50018 (Zaragoza, Spain)<\/p>\n<\/blockquote>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n

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      Alberto Gasc\u00f3n obtained his degree in Industrial Technologies Engineering from the School of Engineering and Architecture (EINA) of the University of Zaragoza (UZ) in 2017. He obtained the Master\u2019s Degree in Industrial Engineering and the Master’s Degree in Electronics Engineering from that same school in 2019 and 2021 respectively.<\/p>\n

      His research interests are focused on the development of Internet of Things (IoT) applications, industry 4.0, sensor networks, pattern recognition and artificial intelligence.\u00a0 He completed his first final project in BSH Home Appliances where he took part in the development of a cooking assistant system. Subsequently, to keep on working on these topics, he joined HOWLab in September 2020 where he started working on a predictive maintenance system for a cash counting machine. Based on this theme, he completed his second final project, currently with a published paper of this work.<\/p>\n

      He is currently working at HOWLab focused on programming microcontrollers, maximizing its autonomy and performance, working with different communication protocols and applying industry 4.0 techniques into machines and also animals, line of research where his future PhD thesis is emplaced.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

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      2022<\/h3>

      Gasc\u00f3n, Alberto; Casas, Roberto; Buldain, David; Marco, \u00c1lvaro<\/p>

      Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City<\/a> Art\u00edculo de revista<\/span> <\/p>

      En: <\/span>Sensors, <\/span>vol. 22, <\/span>no 2, <\/span>2022<\/span>, ISSN: 1424-8220<\/span>.<\/p>

      Resumen<\/a><\/span> | Enlaces<\/a><\/span> | BibTeX<\/a><\/span><\/p>

      @article{s22020586,
      \r\ntitle = {Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City},
      \r\nauthor = {Alberto Gasc\u00f3n and Roberto Casas and David Buldain and \u00c1lvaro Marco},
      \r\nurl = {https:\/\/www.mdpi.com\/1424-8220\/22\/2\/586},
      \r\ndoi = {10.3390\/s22020586},
      \r\nissn = {1424-8220},
      \r\nyear  = {2022},
      \r\ndate = {2022-01-01},
      \r\njournal = {Sensors},
      \r\nvolume = {22},
      \r\nnumber = {2},
      \r\nabstract = {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.},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div>
      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.<\/div>
      Cerrar<\/a><\/p><\/div>