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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 296-300

A New Approach Based on Image Processing for Detection of Wear of Guide-Rail Surface in Elevator Systems

Orhan YAMAN •
Mehmet BAYGIN •
Mehmet KARAKOSE
Publication DateDecember 1, 2016
Volume / IssueVol. , No. (pp. 296-300)
subject

Abstract

Elevators ensure transportation of people inside buildings and increase their life quality. High-rise buildings whose number is increasingly going up today has one or more elevator cabs to provide vertical transportation. A great number of people use elevators in many buildings such as business centres, hotels, hospitals and shopping centres daily. It is highly essential for the elevators used by many people daily to operate constantly. In the event of sudden failure of elevators during operation, people inside them face with a tough situation. Also, people have difficulty during the maintenance-repair period of elevators. Elevator system has counterweight system in order to balance the weight of elevator cab. A guide-rail system has been developed to limit the movements of elevator cab and counterweights on horizontal axis. When an elevator system is operational, cab and counterweight system move reversely. The common failures in elevators are usually seen in the components such as elevator guide-rail system, ropes and motors.  In this study, a system based on image processing has been developed in order to prevent wear on guide-rail surface in elevators. In the proposed method, real-time condition monitoring is performed by cameras using built-in system. The images of elevator guide-rail surface are captured via four digital cameras fixed onto elevator cab. The image-processing methods are applied on the images captured by cameras and hence the wears on the surface of guide-rails are detected. The surface of guide-rail is firstly detected in the proposed method. Then, image segmentation and mathematical morphology are applied on the image of guide-rail surface and the wears on the surface of rail are detected. The failure extent of the wear failures detected are calculated. By processing the images captured by four cameras during movement of elevator, the results for surface of guide-rails are obtained. Using these results, reporting is performed. An elevator prototype has been created in order to carry out tests for development of the proposed method. The tests have been conducted by fixing the built-in system and cameras onto this elevator prototype. It is considerably advantageous to detect the failures on elevator guide-rails through image-processing methods. Following a literature review, it is seen that the proposed method is a new approach.
Keywords:Elevator SystemsFault DetectionImage ProcessingImage Segmentation
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References (21)

Cited Literature
  1. 1

    Serhat, K. Asansör kılavuz ray konsollarının gerilme analizi, ITU / Fen Bil. Ens. / Makine Mühendisliği Anabilim Dalı, Y. L., 2009.

  2. 2

    Ekrem, S. Asansör kılavuz ray konsollarının bilgisayar destekli gerilme analizi, YTU / Fen Bil. Ens. / Makine Müh. / Konstrüksiyon ve İmalat Bilim Dalı, Y. L., 2011.

  3. 3

    Mehmet, A. Asansör kılavuz ray bağlantı elemanlarının deneysel gerilme analizi, ITU / Fen Bil. Ens. / Makine Mühendisliği Anabilim Dalı, Y. L. 2012.

  4. 4

    Serkan, E. Kılavuz ray bağlantı elemanlarının gerilme analizi, ITU / Fen Bilimleri Enstitüsü / Makine Mühendisliği Anabilim Dalı / Konstrüksiyon ve İmalat Bilim Dalı, Yüksek Lisans, 2011.

  5. 5

    Zafer, D. Elektrik kesintilerine karşı asansör kurtarma sistemi tasarımı ve uygulaması, Selçuk Üniversitesi / Fen Bilimleri Enstitüsü, Yüksek Lisans, 2006.

  6. 6

    Landaluze, J. Portilla, I. Cabezón, N. Martínez, A. and Reyero, R. “Application of active noise control to an elevator cabin,” Control Eng. Practice, vol.11, pp. 1423-1431, 2002.

  7. 7

    Peiliang, W. Wuming, H. Wenjun, Y. Fault Diagnosis of Elevator Braking System Based On Wavelet packet algorithm and Fuzzy Neural Network, International Conf. on Electronic Measurement & Ins., pp. 1028 - 1031, 2009.

  8. 8

    Zhang, G. Huang, S. Yuan, Y. The Study of Elevator Fault Diagnosis Based on Multi-Agent System, International Conference on Computational Intelligence and Software Engineering, pp. 1 – 5, 2009.

  9. 9

    Xi, Z. Guo-jun, Z. Yuan-ping, W. Chao-rong, W. Jun-hui, W. Research on the elevator door control system based on the image processing technology, International Conference on Electrical and Control Engineering (ICECE), pp. 1781 – 1784, 2010.

  10. 10

    Chen-Guang, Z. Hong-Yu, X. Liang, J. Research of elevator fault diagnosis based on decision tree and rough set, International Conference on Computer Science and Information Processing (CSIP), pp. 1318 - 1322, 2012.

  11. 11

    Chen, J. Liu, X. AR bispectrum in fault diagnosis of elevator machinery, IEEE Fifth Int. Con. on Advanced Computational Intelligence (ICACI), pp. 1149 – 1152.

  12. 12

    Hu, W. Schroeder, M. Starr, A.G. A Knowledge-Based Real-Time Diagnostic System for PLC Controlled Manufacturing Systems, IEEE International Conference on Systems, Man, and Cybernetics, vol.4, pp. 499 – 504, 1999.

  13. 13

    Yimou, Z. “Study to Elevator inspection market in Deyang city, China ,” Bachelor’s thesiss International business Degree programme, 2012.

  14. 14

    Niu, G. Lee, S.S. Yang, B.S. Lee, S.J. Decision fusion system for fault diagnosis of elevator traction machine, Journal of Mec. Science and Tec., 22(1), 85-95, 2008.

  15. 15

    Zhao F., Koutsoukos, X. Haussecker, H. Reich, J. Cheung, P. Monitoring and fault diagnosis of hybrid systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(6), 1225-1240, 2005.

  16. 16

    Baygin, M. Karakose, M. Immunity-based optimal estimation approach for a new real time group elevator dynamic control application for energy and time saving, The Scientific World Journal, 2013.

  17. 17

    Baygin, M. Karaköse, M. A new intelligent group elevator control approach. In MECHATRONIKA, 2012 15th International Symposium pp. 1-6, 2012.

  18. 18

    Yaman, O. Karaköse, M. Akın, E. Aydın, I. Image processing based fault detection approach for rail surface, In 2015 23nd Signal Processing and Communications Applications Conference (SIU) pp. 1118-1121, 2015.

  19. 19

    Karakose, E. Gencoglu, M.T. Karakose, M. Yaman, O. Aydin, I. Akin, E. A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems, Journal of Intelligent Manufacturing, 1-18, 2015.

  20. 20

    Karhan, M. Oktay, M.O. Karhan, Z. Demir, H. Morfolojik Görüntü İşleme Yöntemleri ile Kayısılarda Yaprak Delen (Çil) Hastalığı Sonucu Oluşan Lekelerin Tespiti, 2011.

  21. 21

    Soille, P. Morphological image analysis: principles and applications, Springer Science & Business Media, 2013.

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How to Cite This Article

O. Y., M. B., M. K. (2016). A New Approach Based on Image Processing for Detection of Wear of Guide-Rail Surface in Elevator Systems. International Journal of Applied Methods in Electronics and Computers, 296-300. https://doi.org/10.18100/ijamec.270559