Research of Image Features for Classification of Wear Debris

• Autorzy: Juránek R. , Machalik S. , Zemčik P.
• Języki publikacji: EN

Abstrakty

EN

The wear debris of engineering equipment (such as combustion engines, gearboxes, etc.) consists of metal particles which can be obtained from lubricants used in the equipment. The analysis of wear particles is very important for early detection and prevention of failures. The analysis is often done using classication of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classication of wear particles based on visual similarity. The main contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. Another contribution is the large public database of particle images which can be used for further experiments. The paper describes the dataset, presents the methods of classication, demonstrates the experimental results, and draws conclusions.

Słowa kluczowe

EN

wear debris; classification; supervised machine learning; SVM; linear regression; features; PCA; HOG; LBP

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2011
• Tom: Vol. 20, No. 4
• Strony: 479--493
• Opis fizyczny: Bibliogr. 16 poz., rys., wykr.

Twórcy

autor

Juránek R.

• Faculty of Information Technology, Brno University of Technology, Brno, CZ

autor

Machalik S.

• Jan Perner Transport Faculty, University of Pardubice, Pardubice, CZ

autor

Zemčik P.

• Faculty of Information Technology, Brno University of Technology, Brno, CZ

Bibliografia

• [1] Papageorgiou, C. P., Oren, M., and Poggio, T. A general framework for object detection. In Proceedings of the International Conference on Computer Vision, page 555, Washington, DC, USA. IEEE Computer Society, 1998.
• [2] Umeda, A., Sugimura, J., and Yamamoto, Y. Characterization of wear particles and their relations with sliding conditions. Wear, 216 (2): 220 - 228, 1998.
• [3] Xu, K., Luxmoore, A. R., Jones, L. M., and Deravi, F. Integration of neural networks and expert systems for microscopic wear particle analysis. Knowledge-Based Systems, 11 (3-4): 213 – 227, 1998.
• [4] Tucker, J. LASERNET FINES Optical Oil Debris Monitor. AD-a347 453. Naval research lab Washington dc laserphysics section and Naval research lab Washington dc laserphysics section, 1998.
• [5] Cho, U., and Tichy, J. A. Quantitative correlation of wear debris morphology: grouping and classification. Tribology International, 33 (7): 461-467, 2000.
• [6] Ojala, T., Pietikänen, M., and Mäenpää, T. Gray scale and rotation invariant texture classification with local binary patterns. In Proceedings of the European Conference on Computer Vision, volume 1, pages 404-420, London, UK, 2000. Springer-Verlag.
• [7] Dalai, N., and Triggs, B. Histograms of oriented gradients for human detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 886-893, Washington, DC, LISA, 2005.
• [8] Podsiadło, P., and Stachowiak, G. Development of advanced quantitative analysis methods for wear particle characterization and classification to aid tribological system diagnosis. Tribology International, 38 (10): 887-897, 2005. Ferrography and Friends - Pioneering Developments in Wear Debris Analysis.
• [9] Raadnui, S. Wear particle analysis-utilization of quantitative computer image analysis: A review. Tribology International, 38 (10): 871 878, 2005. Ferrography and Friends - Pioneering Developments in Wear Debris Analysis.
• [10] Bishop C. M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, 2006, Inc., Secaucus, NJ, USA.
• [11] Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9: 1871-1874, 2008.
• [12] Stachowiak, G. P., Stachowiak, G. W., and Podsiadło, P. Automated classification of wear particles based on their surface texture and shape features. Tribology International, 41 (1): 34-43, 2008.
• [13] Heikkilä, M., Pietikäinen, M., and Schmid, C. Description of interest regions with local binary patterns. Pattern Recogn., 42: 425-436, March, 2009.
• [14] Herout, A., Zemčik, P., Hradiš, M., Juránek, R., Havel, J., Jošth, R., and Žádnik, M. Low-Level Image Features for Real-Time Object Detection, pages 111-136. IN-TECH Education and Publishing, 2010.
• [15] Chang, C.-C., and Lin, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2: 27: 1-27: 27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
• [16] Juránek, R., Machalik, S., and Zemčik, P. Analysis wear debris through classification. In Proceedings of Advanced Concepts of Inteligent Vision Systems (ACIVS’ 2011), LNCS 6915, pages 273-283. Springer Verlag, 2011.