Özet
The final step of the bearing production line constitutes the inspection of the bearing which is mostly performed by visual inspection. Three groups of bearings namely, properly assembled samples, conversely assembled rubber seal and samples where rubber seals were missing are classified using visible range images of these samples. According to the proposed method, extraction of seal regions from the bearing images using circular Hough transform is followed by a higher-order statistical analysis to finalize the classification. Experimental results show that this system may be employed as an assistive tool for bearing inspectors.
Tercüme edilen katkı başlığı | Bearing fault detection method based on statistical analysis and KL distance |
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Orijinal dil | Türkçe |
Ana bilgisayar yayını başlığı | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
Sayfalar | 1881-1884 |
Sayfa sayısı | 4 |
ISBN (Elektronik) | 9781509016792 |
DOI'lar | |
Yayın durumu | Yayınlandı - 20 Haz 2016 |
Etkinlik | 24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Süre: 16 May 2016 → 19 May 2016 |
Yayın serisi
Adı | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
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???event.eventtypes.event.conference??? | 24th Signal Processing and Communication Application Conference, SIU 2016 |
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Ülke/Bölge | Turkey |
Şehir | Zonguldak |
Periyot | 16/05/16 → 19/05/16 |
Bibliyografik not
Publisher Copyright:© 2016 IEEE.
Keywords
- Kullback-Leibler Distance
- bearing
- computer vision
- statistical analysis