Rolling element-bearing feature extraction based on combined wavelets and quantum-behaved particle swarm optimization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
http://link.springer.com/content/pdf/10.1007/s12206-015-0120-3.pdf
Reference13 articles.
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3. P. W. Tse and D. Wang, The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: Part 2 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement—Parts 1 and 2”, Mechanical Systems and Signal Processing, 40 (2) (2013) 520–544.
4. Z. Liu et al., Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings, Neurocomputing, 99 (2013) 399–410.
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