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IoT based prognostics using MEMS sensor with single board computers for rotary machines

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PL
Prognozy oparte na IoT z wykorzystaniem czujnika MEMS z komputerami jednopłytowymi do maszyn rotacyjnych
Języki publikacji
EN
Abstrakty
EN
In the recent past years prognostics has gained importance in the industry sector as they have reduced maintenance cost and time to a great deal. The term IoT has opened a wide variety of applications with it and there have been very few feeble attempts to integrate it with prognostics. Combining low-cost energy efficient MEMS sensor and IoT in prognostics has been a dream far-fetched by industries. This paper will be attempt where the MEMS sensor will be fused along with IoT in prognostics and the same was used in a rotary machine.
PL
W ostatnich latach prognozy zyskały na znaczeniu w sektorze przemysłowym, ponieważ znacznie zmniejszyły koszty i czas konserwacji. IoT otworzył wiele różnych zastosowań i było bardzo niewiele prób zintegrowania go z prognozami. Połączenie niskokosztowego, energooszczędnego czujnika MEMS i Internetu Rzeczy w prognozach było marzeniem naciąganym przez przemysł. Ten artykuł będzie próbą, w której czujnik MEMS zostanie połączony z Internetem Rzeczy w prognostyce i to samo zostanie użyte w maszynie rotacyjnej.
Rocznik
Strony
170--174
Opis fizyczny
Bibliogr. 18 poz.,rys., tab.
Twórcy
  • Department of Mechanical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
autor
  • Department of Mechanical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
autor
  • Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
autor
  • Department of Computer Science and Engineering, Alliance University, Bengaluru, India
Bibliografia
  • [1] A. K. S. Jardine, D. Lin, and D. Banjevic, ‘A review on machinery diagnostics and prognostics implementing conditionbased maintenance’, Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483–1510, Oct. 2006.
  • [2] A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, ‘Rotating machinery prognostics: State of the art, challenges and opportunities’, Mech. Syst. Signal Process., vol. 23, no. 3, pp. 724–739, Apr. 2009.
  • [3] I. Bazovsky, Reliability Theory and Practice. Englewood Cliffs, NJ: Prentice - Hall, 1961.
  • [4] A. K. S. Jardine and A. H. C. Tsang, Maintenance, Replacement, and Reliability: Theory and Applications. CRC Press, Taylor & Francis Group, 2017.
  • [5] M. Sc. Kelly, Maintenance and its management. Farnham, Surrey : Conference Communication, 1989.
  • [6] H. P. Bloch and F. K. Geitner, Machinery Failure Analysis and Troubleshooting - 4th Edition. Butterworth - Heinemann, 2012.
  • [7] J. Mathew and R. J. Alfredson, ‘The Condition Monitoring of Rolling Element Bearings Using Vibration Analysis’, J. Vib. Acoust. Stress Reliab. Des., vol. 106, no. 3, pp. 447–453, Jul. 1984.
  • [8] D. Mba and R. B. K. N. Rao, ‘Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and Rotating Structures.’, Jan. 2006.
  • [9] P. Guo, D. Infield, and X. Yang, ‘Wind Turbine Generator Condition-Monitoring Using Temperature Trend Analysis’, IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 124–133, Jan. 2012.
  • [10] M. Kumar, N. Mohan Misra, and P. Shankar Mukherjee, ‘Advancement and current status of wear debris analysis for machine condition monitoring: a review’, Ind. Lubr. Tribol., vol. 65, no. 1, pp. 3–11, Feb. 2013.
  • [11] D. Jung, Z. Zhang, and M. Winslett, ‘Vibration Analysis for IoT Enabled Predictive Maintenance’, in 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017, pp. 1271–1282.
  • [12] F. Al-Badour, M. Sunar, and L. Cheded, ‘Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques’, Mech. Syst. Signal Process., vol. 25, no. 6, pp. 2083–2101, Aug. 2011.
  • [13] J. d J. Rangel-Magdaleno, R. d J. Romero-Troncoso, R. A. Osornio-Rios, E. Cabal-Yepez, and A. Dominguez-Gonzalez, ‘FPGA-Based Vibration Analyzer for Continuous CNC Machinery Monitoring With Fused FFT-DWT Signal Processing’, IEEE Trans. Instrum. Meas., vol. 59, no. 12, pp. 3184–3194, Dec. 2010.
  • [14] T. Teslyuk, P. Denysyuk, A. Kernytskyy, and V. Teslyuk, ‘Automated control system for arduino and android based intelligent greenhouse’, in 2015 XI International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), 2015, pp. 7–10.
  • [15] Y. J. Chan and J.-W. Huang, ‘Multiple-point vibration testing with micro-electromechanical accelerometers and microcontroller unit’, Mechatronics, vol. 44, pp. 84–93, Jun. 2017.
  • [16] Morgenthal Guido, Rau Sebastian, Taraben Jakob, and Abbas Tajammal, ‘Determination of Stay-Cable Forces Using Highly Mobile Vibration Measurement Devices’, J. Bridge Eng., vol. 23, no. 2, p. 04017136, Feb. 2018.
  • [17] S. Korkua, H. Jain, W. Lee1, and C. Kwan, ‘Wireless health monitoring system for vibration detection of induction motors’, in 2010 IEEE Industrial and Commercial Power Systems Technical Conference - Conference Record, 2010, pp. 1–6.
  • [18] J. K. Sinha and K. Elbhbah, ‘A future possibility of vibration based condition monitoring of rotating machines’, Mech. Syst. Signal Process., vol. 34, no. 1, pp. 231–240, Jan. 2013
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-417c4add-986d-4a67-ac93-e5bd0ea5f322
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