Condition Identification of Bolted Joints Based on Autoregressive Model

Yan Zhang; Guo Ying Zeng; Deng Feng Zhao; Ming Yan Li

doi:10.4028/www.scientific.net/AMR.433-440.617

Paper Titles

The Research and Development of Automotive Engine Test Platform
p.595

Research on Sealing Technology of Magnetic Fluid in Medicinal High-Speed Grinder Gearbox
p.600

Estimating the Threading Dislocation Density of Thick GaN Films
p.606

Robust Variable Structure Control for Compound Active-Clamp Three-Phase PFC Converter with Soft Switch
p.610

Condition Identification of Bolted Joints Based on Autoregressive Model
p.617

Based on Fuzzy Logic Control of Recovery of the Method Research of Maximum Power Point Tracking
p.622

Multifunction GPS/GPRS Automobile Security System
p.629

Establishment of Cutting Tools’ Processing Angle Model in Whirling Milling
p.635

A New Ti-Based Amorphous Powder Synthesized by Mechanical Alloying
p.642

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Condition Identification of Bolted Joints Based on...

Condition Identification of Bolted Joints Based on Autoregressive Model

Abstract:

The difference signals, which between upper and lower shells of flange bolted joints structure, was applied to establish the autoregressive model for system condition identification. Firstly, an AR model is built by difference signals. The established AR model is used as a filter to process the difference signal in test state under the same condition and output residual series. Then the statistical parameters, such as Itakura distance, skewness, kurtosis and variance, are used to handle residual series. The results of experiment show that Itakura distance is a useful eigenvalue to identify the bolted joints condition.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

617-621

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.617

Citation:

Cite this paper

Online since:

January 2012

Authors:

Yan Zhang, Guo Ying Zeng, Deng Feng Zhao, Ming Yan Li

Keywords:

Autoregressive Model, Bolted Joint, Condition Identification, Itakura Distance

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Dong Guangming, Chen Jin, et al. Experimental study on diagnosing the attachment bolt looseness in a clamping support based on Wavelet Packet Transformation and Neural Network, Mechanical Science and Technology, vol. 25, January 2006, pp.102-106.

Google Scholar

[2] Li Yong, Wang Xi-yang and Wang Xue-chao, Diagnosis of milling cutter condition based on AR model, Failure Analysis and Prevention, vol. 4, 2009, pp.24-29, February.

Google Scholar

[3] Xiyang Wang, Viliam Makis, Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test, Journal for Sound and Vibration, vol. 327, 2009, pp.413-423.

DOI: 10.1016/j.jsv.2009.07.004

Google Scholar

[4] Wenyi Wang, Albert K. Wong, Autoregressive model-based gear fault diagnosis, Journal of Vibration and Acoustics, vol. 124, April 2002, pp.172-179.

DOI: 10.1115/1.1456905

Google Scholar

[5] H. Endo, R. B. Randall, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mechanical Systems and Signal Processing, vol. 21, 2007, p.906–919.

DOI: 10.1016/j.ymssp.2006.02.005

Google Scholar

[6] Yang Shuzi and Wu Ya, Time series analysis in engineering application, 2nd ed, Huazhong University of Sinence and Technology Press Co. Ltd, Wu Han, June (2007).

Google Scholar