Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems

Hasan Yetiş [1] , Mehmet Karaköse [2]


Continuity of production is a highly important in the days that manufacturing is becoming bigger and serial. The mistakes done while producing process cause fail on products and it may bring about even big losses for the facility. Furthermore, hitches on robots at production line may also cause crucial damages that may give rise to high repair costs and discontinuance of production. In this study, it is aimed to obtain alive bird's eye view map of production lines, which are big and impossible to be monitored with only one camera, by using multi cameras and stitching algorithms. Finding the similar scenes of input images, estimation of homography, warping and blending operations, which are the steps used in feature based image-stitching algorithms, are applied respectively on images that are taken by cameras. The assignment of second nearest neighbor distance rate adaptively makes the results more qualified. After obtaining single stitched image movement detection is actualized by using the difference of sequential frames, and anomaly movements are determined. As a result, the robots at the long production lines can be monitored in one screen, and with processing the obtained image, faults on robots that may cause damage at non-cheap machines can be handled before time.

Condition monitoring, Multi robots, Production lines, Image mosaicing, Image processing, Fault Diagnosis
  • [1] G. Divya, and C. Chandrasekhar, “Image Mosaicing Technique for Wide Angle Panorama,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 15, pp. 420-429, 2015.
  • [2] M. Lin, G. Xu, X. Ren, and K. Xu, “Cylindrical Panoramic Image Stitching Method Based On Multi-cameras,” The 5th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Shenyang, China, pp. 1091-1096, June 2015.
  • [3] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [4] C. M. Huang, S. W. Lin, and J. H. Chen, “Efficient Image Stitching of Continuous Image Sequence with Image and Seam Selections,” IEEE Sensors Journal, vol. 15, pp. 5910-5918, 2015.
  • [5] R. Abraham, and P. Simon, “Review on Mosaicing Techniques in Image Processing,” International Conference on Advanced Computing & Communication Technologies, Rohtak, India, pp. 63-68, April 2013.
  • [6] S. Lee, Y. Park, and D. Lee, “Seamless Image Stitching Using Structure Deformation with HoG Matching,” International Conference on Information and Communication Technology Convergence (ICTC), Jeju, South Korea, pp. 933-935, Oct. 2015.
  • [7] Z. Qui, P. Shi, X. Jiang, D. Pan, C. Feng, and Y. Sha, “Image Stitching and Ghost Elimination Based on Shape-Preserving Half-Projective Warps,” International Conference on Information and Automation, Lijiang, China, pp. 2610-2615, Aug. 2015.
  • [8] A. Laraqui, A. Baataoui, A. Saaidi, A. Jarrar, M. Masrar, and K. Satori, “Image mosaicing using voronoi diagram,” Multimedia Tools and Applications, pp. 1-27, 2016.
  • [9] I. Aydin, M. Karakose, and E. Akin, “An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space,” ISA Transaction, vol. 53, pp. 220-229, 2014.
  • [10] I. Aydin, E. Karakose, M. Karakose, M. T. Gencoglu, and E. Akin, “A New Computer Vision Approach for Active Pantograph Control,” IEEE International Symposium on Innovations in Intelligent Systems and Applications (IEEE INISTA 2013), Albena, Bulgaria, 2013.
  • M. Baygin, and M, Karakose, “A new image stitching approach for resolution enhancement in camera arrays,” 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, pp. 1186-1190, Nov. 2015.
  • P. M. Jain, and V. K. Shandliya, “A Review Paper on Various Approaches for Image Mosaicing,” International Journal of Computational Engineering Research, vol. 3, pp. 106-109, 2013.
  • H. Joshi, and K. Sinha, “A Survey on Image Mosaicing Techniques,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2, pp. 365-369, 2013.
  • D. Ghosh, and N. Kaabouch, “A Survey on Image Mosaicing Techniques,” Journal of Visual Communication and Image Representation, vol. 32, pp. 1-11, 2016.
  • I. K. Sarangi, and S. Nayak, “Image Mosaicing of Panoramic Images,” Bachelor Thesis, National Institute of Technology, Rourkela, 2014.
  • V. K. S. Prathap, S. A. K. Jilani, and P. R. Reddy, “A Critical Review on Image Mosaicing,” International Conference on Computer Communication Informatics (ICCCI), Coimbatore, India, pp. 1-8, Jan. 2016.
  • J. Krizaj, V. Struc, and N. Pvesic, “Adaptation of SIFT Features for Robust Face Recognition,” International Conference on Image Analysis and Recognition (ICIAR), Povoa de Varzim, Portugal, pp. 1-10, June 2010.
  • H. Yetis, M. Baygin, and M. Karakose, “A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines,” 5th International Conference on Manufacturing Engineering and Process (ICMEP), Istanbul, Turkey, 2016.
  • D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
  • S. Mistry, and A. Patel, “Image Stitching using Harris Feature Detection,” International Research Journal of Engineering and Technology (IRJET), vol. 3, pp. 1363-1369, 2016.
  • A. Levin, A. Zomet, S. Peleg, and Y. Weiss, “Seamless Image Stitching in the Gradient Domain,” Computer Vision (ECCV), pp. 377-389, 2004.
  • Y. Santur, M. Karakose, I. Aydin, E. Akin, “IMU based adaptive blur removal approach using image processing for railway inspection,” International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, pp. 1-4, May 2016.
  • H. Yetis, and M. Karakose, “Image Mosaicing Based Condition Monitoring Approach for Multi Robots at Production Lines in Industrial Autonomy Systems,” 3rd International Conference on Advanced Technology & Sciences (ICAT 16), Istanbul, Turkey, 2016.
Subjects Engineering
Journal Section Research Article
Authors

Author: Hasan Yetiş
Institution: FIRAT UNIV
Country: Turkey


Author: Mehmet Karaköse
Institution: FIRAT UNIV
Country: Turkey


Dates

Publication Date : December 1, 2016

Bibtex @conference paper { ijamec270410, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {}, eissn = {2147-8228}, address = {}, publisher = {Selcuk University}, year = {2016}, volume = {}, pages = {271 - 276}, doi = {10.18100/ijamec.270410}, title = {Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems}, key = {cite}, author = {Yetiş, Hasan and Karaköse, Mehmet} }
APA Yetiş, H , Karaköse, M . (2016). Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers , (Special Issue-1) , 271-276 . DOI: 10.18100/ijamec.270410
MLA Yetiş, H , Karaköse, M . "Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems". International Journal of Applied Mathematics Electronics and Computers (2016 ): 271-276 <https://dergipark.org.tr/en/pub/ijamec/issue/25619/270410>
Chicago Yetiş, H , Karaköse, M . "Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems". International Journal of Applied Mathematics Electronics and Computers (2016 ): 271-276
RIS TY - JOUR T1 - Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems AU - Hasan Yetiş , Mehmet Karaköse Y1 - 2016 PY - 2016 N1 - doi: 10.18100/ijamec.270410 DO - 10.18100/ijamec.270410 T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 271 EP - 276 VL - IS - Special Issue-1 SN - -2147-8228 M3 - doi: 10.18100/ijamec.270410 UR - https://doi.org/10.18100/ijamec.270410 Y2 - 2016 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems %A Hasan Yetiş , Mehmet Karaköse %T Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems %D 2016 %J International Journal of Applied Mathematics Electronics and Computers %P -2147-8228 %V %N Special Issue-1 %R doi: 10.18100/ijamec.270410 %U 10.18100/ijamec.270410
ISNAD Yetiş, Hasan , Karaköse, Mehmet . "Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems". International Journal of Applied Mathematics Electronics and Computers / Special Issue-1 (December 2016): 271-276 . https://doi.org/10.18100/ijamec.270410
AMA Yetiş H , Karaköse M . Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 271-276.
Vancouver Yetiş H , Karaköse M . Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 276-271.