Abstract
This paper suggests an image gradient based method that determines the optimal image size for convolutional neural network (CNN)-based diagnosis of fluid-film rotorbearing systems. As distinct patterns improve the diagnosis performance, a criterion is defined to measure the intensity of patterns in an image. The proposed criterion is derived by segmenting an image by the size of the CNN filter and evaluating each segment through the use of image gradient analysis. Vibration signals from a testbed are used to demonstrate the proposed method. First, the signals are transformed into vibration images by using an omnidirectional regeneration technique. Then, vibration images of four different health states are analyzed using the suggested criterion. The analyzed results are compared to the performance of CNN based diagnosis. The results indicate that the proposed criterion can determine the optimal size range of the vibration image that gives the best performance for CNN-based diagnosis.
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Acknowledgments
This research was supported by National Research Foundation (NRF) (Project No. NRF-2018M2A8A4023312), Korea Institute of Machinery and Materials (KIMM), and the 2017 Open R&D Program of Korea Electric Power Corporation (KEPCO) under Grant R17tH02.
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Recommended by Editor No-cheol Park
Byeng Dong Youn received the B.S. degree in Mechanical Engineering from Inha University, Incheon, South Korea, in 1996, the M.S. degree in Mechanical Engineering from Korea Advanced Institute of Science & Technology, Daejeon, South Korea, in 1998, and the Ph.D. degree in Mechanical Engineering from the University of Iowa, Iowa City, IA, USA, in 2001. He is a Professor with the Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South Korea. Dr. Youn was the recipient of ASME IDETC Best Paper Awards (2001 and 2008), the ISSMO/Springer Prize for a Young Scientist (2005), the IEEE PHM Competition Winner (2014), the PHM Society Data Challenge Competition Winner (2014, 2015, 2017, 2019), etc.
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Jeon, B.C., Jung, J.H., Kim, M. et al. Optimal vibration image size determination for convolutional neural network based fluid-film rotor-bearing system diagnosis. J Mech Sci Technol 34, 1467–1474 (2020). https://doi.org/10.1007/s12206-020-0308-z
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DOI: https://doi.org/10.1007/s12206-020-0308-z