Deep Learning Based Diagnostics of Orbit Patterns in Rotating Machinery



Haedong Jeong Sunhee Woo Suhyun Kim Seungtae Park Heechang Kim Seungchul Lee


Vibration-based orbit analysis has been employed as a powerful tool in diagnosing the operating state for rotating machinery in power plants. However, due to the difficulties of extracting mathematical features for data-driven approaches in the orbit analysis, it heavily depends on the expert knowledge or experience. In this paper, the deep learning algorithm in machine learning is used to develop autonomous orbit pattern recognition. In details, the convolutional neural network is implemented to build up weights between convolution kernels and pixels, and to construct the entire structure of the neural networks. Finally, the trained network enables us to classify the shapes of the orbit via orbit shape images and its result can estimate fault modes of the rotating machinery. The proposed framework is demonstrated with a rotating testbed.

How to Cite

Jeong, H., Woo, S., Kim, S., Park, S., Kim, H., & Lee, S. (2016). Deep Learning Based Diagnostics of Orbit Patterns in Rotating Machinery. Annual Conference of the PHM Society, 8(1).
Abstract 40 | PDF Downloads 170



machine learning, rotating machinery, deep learning, Convolutional Neural Networks, Orbit Analysis, Image Pattern Recognition

Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, vol. 20, pp. 1483-1510.
Robert, C. E. (1998). Machinery malfunction diagnosis and correction: Vibration analysis and troubleshooting for the process industries. Prentice Hall.
Morgan, E. (2014). Steam turbine seal rub: Vibration data helps to identify a steam turbine seal rub. Orbit Magazine.
Duda, R. O., Peter, E. H., & David, G. S. (2012). Pattern classification. John Wiley & Sons.
Yang, B. S., Lim, D. S., & Tan, A. C. C. (2005). VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Systems with Applications, vol. 28 (4), pp. 735-742.
Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, vol. 21 (6), pp. 2560-2574.
Jacek, M. Z. (1992). Introduction to artificial neural systems. St. Paul: West publishing company.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, vol. 38 (3), pp. 1876-1886.
Yan, C., Zhang, H., & Wu, L. (2010). Automatic recognition of orbit shape for fault diagnosis in steam turbine generator sets. Journal of Computational Information Systems, 6(6), 1995-2008.
LeCun, Y., Begio, Y., & Hinton, G. (2015). Deep learning. Nature, vol. 521, pp. 436-444.
Rosenfeld, A. (1981). Image pattern recognition. Proceedings of the IEEE, vol. 69 (5), pp. 596-605.
Technical Papers