Abstract
Rub-impact faults of rotor blades are commonly observed in rotating machines, which may cause considerable damage to the equipment. The complex nature of rubbing faults makes it difficult to utilize conventional feature extraction techniques for their diagnosis. In this paper, a new method is proposed for the diagnosis of rub-impact faults of different intensities, using a one-against-all multiclass support vector machines (OAA-MCSVM) classifier that is trained on hybrid features extracted from the intrinsic mode functions (IMFs) of the vibration signal. The raw vibration signal is decomposed into IMFs using empirical mode decomposition (EMD). For each IMF, features such as its total energy and its energy as a fraction of the original vibration signal are calculated directly; whereas its frequency-domain features are extracted by first calculating its frequency spectrum using the fast Fourier transform (FFT). The proposed approach is tested on rubbing fault data obtained using an experimental testbed. The results demonstrate that this approach is effective in differentiating no-rubbing condition and rubbing occurring with various intensities.
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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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Prosvirin, A., Kim, J., Kim, JM. (2019). Efficient Rub-Impact Fault Diagnosis Scheme Based on Hybrid Feature Extraction and SVM. 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_37
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DOI: https://doi.org/10.1007/978-981-13-0341-8_37
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