{"id":5431,"date":"2023-11-22T10:25:48","date_gmt":"2023-11-22T08:25:48","guid":{"rendered":"https:\/\/howlab-dev.i3a.es\/?p=5431"},"modified":"2023-11-22T10:25:50","modified_gmt":"2023-11-22T08:25:50","slug":"alvaro-marco-marco","status":"publish","type":"post","link":"https:\/\/howlab.i3a.es\/es\/alvaro-marco-marco\/","title":{"rendered":"\u00c1lvaro Marco Marco"},"content":{"rendered":"
\t\t\t\t
\n\t\t\t\t\t\t\t
  • \n\t\t\t\t\t
    \n\t\t\t
    \n\t\t\t\t

    Investigadores<\/a><\/h3>\n

    \u00c1lvaro Marco Marco<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t\t\t
    1. <\/a><\/li>\n\t\t\t\t\t\t\t\t\t<\/ol>\n\n\t\t\t\t
      \n\t\t\t\t\t\n\t\t\t\t\t\t<\/em>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\n\t\t\t\t
      \n\t\t\t\t\t\n\t\t\t\t\t\t<\/em>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n\n
      \n
      \n\t\t\"\"\n\t<\/div>\n\n<\/div><\/div>
      \t\n\t\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n<\/div><\/div><\/div><\/div>
      \n
      \n\t
      \n

      Research Staff, Lecturer<\/em><\/p>\n

      Tel\u00e9fono:<\/strong> +34 876 555 464<\/p>\n

      Email:<\/strong> amarco@unizar.es, amarco@geoslab.com<\/p>\n

      Address:<\/strong> Lab. 5.1.10, 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

      \n\t\t\t\t\t\t\t
      \n\t\t\t\t\t\tABOUT ME\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t
      \n\t\t\t\t\t\t
      <\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n<\/div><\/div>
      \n
      \n\t

      \u00c1lvaro Marco is a Researcher & Development Engineer in GeospatiumLab, a software company based in Zaragoza, Spain, and a partial-time lecturer at University of Zaragoza.<\/p>\n

      He received an M.S. degree in electrical engineering in 2000 and a Ph.D. degree in electronic engineering in 2007, both from the University of Zaragoza, Zaragoza, Spain.<\/p>\n

      \u00c1lvaro joined Howlab in 2011 when the group was established, and he has participated in more than 60 R&D projects for public administrations and private companies, holding 14 patents and having published over 60 scientific papers, including both conference and journal\u2019s indexed papers.\u00a0 He has served as editorial board member on International Journal of Distributed Sensor Networks and on Wireless Computing and Mobile Communications and is also TPC member of several international conferences.<\/p>\n

      His interests include Machine Learning, IoT, sensor networks, ambient intelligence, and middleware applications.<\/p>\n

      CV Universidad de Zaragoza<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

      \n\t\t\t\t\t\t\t
      \n\t\t\t\t\t\tPUBLICATIONS\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t
      \n\t\t\t\t\t\t
      <\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n<\/div><\/div>
      \n
      \n\t
      <\/a>
      \r\n
      58 registros<\/span> «<\/a> ‹<\/a> 1 de 12 ›<\/a> »<\/a> <\/div><\/div>

      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>