Abstract
Bearing fault diagnosis is an inevitable process in the maintenance of rotary machines. Multiple combined defects in bearings are more difficult to detect because of the complexity in components of acquired acoustic emission signals. To address this issue, this paper proposes a deep learning method that can effectively detect the combined defects in bearings. The proposed deep neural network (DNN) is based on the stacked denoising autoencoder (SDAE). In this study, the proposed method trains single faulty data while it efficiently classifies multiple combined faults. Experimental results indicate that the proposed method achieves an average accuracy of 91% although it only has single mode fault information.
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Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
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Duong, B.P., Kim, JM. (2019). Fault Diagnosis of Multiple Combined Defects in Bearings Using a Stacked Denoising Autoencoder. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_8
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DOI: https://doi.org/10.1007/978-981-13-0341-8_8
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