3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis

      data-paper

      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

          Rotating machines are often operated under various operating conditions. However, the characteristics of the data varies with their operating conditions. This article presents the time-series dataset, including vibration, acoustic, temperature, and driving current data of rotating machines under varying operating conditions. The dataset was acquired using four ceramic shear ICP based accelerometers, one microphone, two thermocouples, and three current transformer (CT) based on the international organization for standardization (ISO) standard. The conditions of the rotating machine consisted of normal, bearing faults (inner and outer races), shaft misalignment, and rotor unbalance with three different torque load conditions (0 Nm, 2 Nm, and 4 Nm). This article also reports the vibration and driving current dataset of a rolling element bearing under varying speed conditions (680 RPM to 2460 RPM). The established dataset can be used to verify newly developed state-of-the-art methods for fault diagnosis of rotating machines. Mendeley Data. DOI: 10.17632/ztmf3m7h5x.6, DOI: 10.17632/vxkj334rzv.7, DOI: 10.17632/x3vhp8t6hg.7, DOI: 10.17632/j8d8pfkvj2.7

          Related collections

          Most cited references7

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

          Rolling element bearing diagnostics—A tutorial

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

            Applications of machine learning to machine fault diagnosis: A review and roadmap

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

              Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

                Bookmark

                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                09 March 2023
                June 2023
                09 March 2023
                : 48
                : 109049
                Affiliations
                [a ]Department of Mechanical Engineering, Center for Noise and Vibration Control Plus, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Daejeon, Yuseong-gu 34141, South Korea
                [b ]Automotive R&D Division, Hyundai Motor Group, 150, HyundaiYeonguso-ro, Namyang-eup, Hwaseong-si, Gyeonggi-do 18280, South Korea
                Author notes
                [* ]Corresponding author. yhpark@ 123456kaist.ac.kr
                Article
                S2352-3409(23)00167-1 109049
                10.1016/j.dib.2023.109049
                10036499
                36969976
                352be66a-ae3f-4c09-ba97-19580cda5129
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 February 2023
                : 2 March 2023
                Categories
                Data Article

                ball bearing,unbalance,misalignment,load fluctuation,speed fluctuation,condition monitoring

                Comments

                Comment on this article