Abstract
In this paper, a novel global non-destructive evaluation (NDE) technique based on information fusion is proposed to diagnose loosening fault of clamping support. Two feature extraction methods are used to extract feature, which are wavelet package transform and power spectrum density analysis. During diagnosing loosening fault, two local decisions are made by using WP feature and PSD feature respectively. Then the two features are fused to make another local decision. Lastly, the three local decisions are fused to make global decision. The information fusion result have high correct diagnosis ratio and good antinoise performance. The correct diagnosis ratios with no noise and random noise reach 94.3% and 88.6% respectively.
This work is supported by Natural Science Foundation of China (No. 50335030 and No. 10176014)
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, C.Z., et al.: Structure Damage Detection and Intelligent Diagnosis. Science press (2001)
Doebling, S.W., Farrar, C.R., Prime, M.B.: A Summary Review of Vibration-based Damage Identification Methods. The Shock and Vibration Digest 30, 91–105 (1998)
Sohn, H., Farrar, C.R.: Damage Diagnosis Using Time Series Analysis of Vibration Signals. Smart Materials and Structures 10, 1–6 (2001)
Robertson, A.N., Farrar, C.R., Sohn, H.: Singularity Detection for Structural Health Monitoring Using Holder Exponents. Mechanical Systems and Signal Processing 17, 1163–1184 (2003)
Kwanghee, S.L.: Diagnosis of Rotating Machines by Utilizing A Backpropagation Neural Net (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, W., Chen, J., Wu, X., Li, F., Zhang, G., Dong, G.M. (2005). Early Loosening Fault Diagnosis of Clamping Support Based on Information Fusion. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_96
Download citation
DOI: https://doi.org/10.1007/11427469_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)