Abstract
Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a new intelligent fault diagnosis scheme based on Wavelet Packet Transform (WPT) and Extreme Learning Machine (ELM) is proposed. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vector input to the classifiers. A novel classifier, ELM, and its variants, error-minimized ELM (EM-ELM) and online sequential ELM (OS-ELM), were introduced in this study to diagnose the fault on bearings. ELM has been proved to be extremely fast and can provide good generalization performance on many pattern recognition cases. However, preliminary ELM is a batch learning algorithm with a fixed network structure. EM-ELM and OS-ELM are the extends of the preliminary ELM to allow network to grow in the learning process and to learn data sequentially. ELM, EM-ELM and OS-ELM classifiers were evaluated on 13 fault datasets and the empirical results showed that they are really fast and perform well on all the 13 datasets.
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© 2015 Springer International Publishing Switzerland
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Lan, Y., Xiong, X., Han, X., Huang, J. (2015). Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_21
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DOI: https://doi.org/10.1007/978-3-319-14066-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
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