5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

          Related collections

          Most cited references 33

          • Record: found
          • Abstract: not found
          • Article: not found

          A review on machinery diagnostics and prognostics implementing condition-based maintenance

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Artificial intelligence for fault diagnosis of rotating machinery: A review

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Industry 4.0: A survey on technologies, applications and open research issues

               Yang Lu (2017)
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 April 2021
                April 2021
                : 21
                : 8
                Affiliations
                [1 ]Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain; itziar.martija@ 123456ehu.eus (I.M.); aitor.garrido@ 123456ehu.eus (A.J.G.); izaskun.garrido@ 123456ehu.eus (I.G.)
                [2 ]Intenance, RDT Company, 48100 Munguia, Spain; iker.lopez@ 123456intenance.com
                [3 ]Engineering School of Gipuzkoa, University of the Basque Country (UPV/EHU), 20600 Eibar, Spain; patxi.alkorta@ 123456ehu.eus
                Author notes
                [* ]Correspondence: fcjavier.maseda@ 123456ehu.eus ; Tel.: +34-94-6014354
                Article
                sensors-21-02762
                10.3390/s21082762
                8070775
                d536b400-8d9f-493f-8a2d-d4faf2a71c38
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Comments

                Comment on this article