Authors: Shujie Liu Yawei Hu Chao Li Huitian Lu Hongchao Zhang
Publish Date: 2015/02/05
Volume: 28, Issue: 4, Pages: 1045-1055
Abstract
The soft failure of mechanical equipment makes its performance drop gradually which occupies a large proportion and has certain regularity The performance can be evaluated and predicted through early state monitoring and data analysis In this paper the support vector machine SVM a novel learning machine based on the VC dimension theory of statistical learning theory is described and applied in machinery condition prediction To improve the modeling capability wavelet transform WT is introduced into the SVM model to reduce the influence of irregular characteristics and simultaneously simplify the complexity of the original signal The paper models the vibration signal from the double row bearing and wavelet transformation and SVM model WT–SVM model is constructed and trained for bearing degradation process prediction Besides Hazen plotting position relationships is applied to describe the degradation trend distribution and a 95 confidence level based on tdistribution is given The single SVM model and neural network NN approach is also investigated as a comparison The modeling results indicate that the WT–SVM model outperforms the NN and single SVM models and is feasible and effective in machinery condition predictionThis work is jointly supported by the Natural Science Foundation of China No 51205043 the Basic Research and Development Plan of China No 2011CB013401 and the Special Fundamental Research Funds for Central Universities of China No DUT14QY21
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