Logo PTI Logo FedCSIS

Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 26

Medical Steel Fault Prediction Using Deep Learning Techniques

, , ,

DOI: http://dx.doi.org/10.15439/2021F2

Citation: Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 26, pages 153158 ()

Full text

Abstract. This research work focus on the assessment and evaluation of fault detection using deep learning techniques. The evaluation is made accordingly using Deep CNN with the variants corresponding to simple CNN, Resnet, Alexnet and Vgg\_16. Besides, classification accuracy is improved by parameter optimizing and sample size equalization strategy. Experimental results shows that evaluation using the proposed methods with Vgg\_16 gives an improved training accuracy of about 90\% and validation accuracy of about 87\%. This proves that fault detection and analysis in medical equipments and transplanting devices can be efficiently identified for better treatment and device management


  1. Isermann, R. Model-based fault-detection and diagnosis—status and applications. Annu. Rev. Control 29 (1) pp. 71–85, 2005.
  2. Dong, H., Wang, Z., & Gao, H. Fault detection for markovian jump systems with sensor saturations and randomly varying nonlinearities, Circuits and Systems I: Regular Papers. IEEE Transactions on 59 (10) pp. 2354–2362, 2012.
  3. Yin, S., Ding, S.X., Haghani, A., Hao, H., & P. Zhang. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 22 (9) pp. 1567–1581, 2012.
  4. Widodo, A., & Yang, B.S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21 (6) pp. 2560–2574, 2007.
  5. Basheer, I., & Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Methods 43 (1) pp. 3–31, 2000.
  6. Yin, S., Ding, S., Xie, X., & Luo, H. A review on basic data-driven approaches for industrial process monitoring, IEEE Trans. Ind. Electron. 61 (11) 6418–6428, 2014.
  7. Du, W., & Zhan, Z. Building decision treeclassifieronprivatedata, in: Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, vol. 14, Australian Computer Society, Inc., pp. 1–8, 2002.
  8. Zou, H., Hastie, T., & Tibshirani, R. Sparse principal component analysis, J. Comput. Graphical Stat. 15 (2) pp. 265–286, 2006.
  9. Braga, J., Heuze, Y., Chabadel, O., Sonan, N., & Gueramy, A. Non-adult dental age assessment: correspondence analysis and linear regression versus Bayesian predictions, Int. J. Legal Med. 119 (5) pp. 260–274, 2005.
  10. Russell, E.L., Chiang, L.H., & Braatz, R.D. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis, Chemom. Intell. Lab. Syst. 51 (1) pp. 81–93, 2000.
  11. Bach, F.R., & Jordan, M.I. Kernel independent component analysis, J. Mach. Learn. Res. 3 pp. 1–48, 2003.
  12. Yin, S., Zhu, X., & Kaynak, O. Improved pls focused on key performance indictor related fault diagnosis, IEEE Trans. Ind. Electron. 2014.
  13. Amit ,Y., & Geman, D. “Shape quantization and recognition with randomized trees,” Neural Comput., vol. 9, no. 7, pp. 1545–1588, 1997.
  14. Breiman, L. “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  15. Breiman, L. “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123– 140, 1996.
  16. Dong, Y., Du, B., & Zhang ,L."Target Detection Based on Random Forest Metric Learning," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1830-1838, April 2015.
  17. Good, R.P., Kost, D., & Cherry, G.A. "Introducing a Unified PCA Algorithm for Model Size Reduction," in IEEE Transactions on Semiconductor Manufacturing, vol. 23, no. 2, pp. 201-209, May 2010.
  18. Bewick, V., L. Cheek andJ. Ball, Statistics review 14: Logistic regression. Crit Care 9: 112-118. http://dx.doi.org/10.1186/cc3045, 2005.
  19. Breiman, L., Classification and Regression Trees. 1st Edn., Wadsworth International Group, Belmont, ISBN-10: 0534980538 pp: 358, 1984.
  20. Buscema, M., S. Terzi and W. Tastle, A new meta- classifier. Proceedings of the North American Fuzzy Inform Processing Society, Jul. 12-14, IEEE Xplore Press, Toronto, pp: 1-7. http://dx.doi.org/10.1109/NAFIPS.2010.5548298, 2010.
  21. Chaudhuri, B.B. and U. Bhattacharya, Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing, 34: 11-27. DOI: 10.1016/S0925-2312(00)00305-2, 2000.
  22. Dong, L., D. Xiao, Y. Liang and Y. Liu, Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Elec. Power Syst. Res., 78: 129-136. http://dx.doi.org/10.1016/J.EPSR.2006.12.013, 2008.
  23. Eslamloueyan, R., Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process. Applied Soft Comput., 11: 1407-1415. http://dx.doi.org/10.1016/J.ASOC.2010.04.012, 2011.
  24. Haykin, S.S., Neural Networks: A Comprehensive Foundation. 1st Edn., Macmillan, New York, ISBN-10: 0023527617, pp: 69, 1994.
  25. Zhang, Y., Jiang, J, Bibliographical review on reconfigurable fault-tolerant control systems. Annual Rev. Control, 32: 229-252. http://dx.doi.org/10.1016/J.ARCONTROL.2008.03.008, 2008.
  26. Maurya, M.R., Rengaswamya, R., Venkatasubramanian, V., Fault diagnosis using dynamic trend analysis: A review and recent developments. Eng. Appli. Artif. Intell., 20: 133-146. http://dx.doi.org/10.1016/j.engappai.2006.06.020, 2007.
  27. Lo, C.H., Wong, Y.K., Rad, A.B., Chow, K.M., Fusion of qualitative bond graph and genetic algorithms: A fault diagnosis application. ISA Trans., 41: 445-456. 10.1016/S0019-0578(07)60101-3, 2002.