Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

Hai Yang Jiang; Hua Qing Wang; Peng Chen

doi:10.4028/www.scientific.net/AMM.470.683

Paper Titles

Nonlinear Model-Based Control for Vacuum Altitude Simulator
p.663

Study on RBF Neural Network PID Control for Hydro-Viscous Drive System
p.668

Application of Wireless Communication Technology in the Detection and Debugging for Control System on Bullet Trains
p.673

Research on the Technology of Two-Axis Synchronization Control Based on Fuzzy PID
p.677

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network
p.683

PLC-Based Stamp Puncher
p.689

Application of Nondestructive Testing in Quality Controlling of High-Density Polyethylene Silicore Plastic Duct
p.693

Study on CCT Curve and Microstructure Evolution of HRB500E
p.699

An Application of the Common Maintenance Time in the System Maintenance Allocation
p.703

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 470Fault Detection and Discrimination of Rotating...

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

Abstract:

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

You might also be interested in these eBooks

Mechanical Engineering, Materials Science and Civil Engineering II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 470)

Pages:

683-688

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.470.683

Citation:

Cite this paper

Online since:

December 2013

Authors:

Hai Yang Jiang, Hua Qing Wang, Peng Chen

Keywords:

Bayesian Network (BN), Non-Dimensional Symptom Parameters, Rotating Machinery, Vibration Signal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] P. CHEN, T. TOYOTA, Fuzzy Diagnosis and Fuzzy Navigation for Plant Inspection and Diagnosis Robot, Proc. of FUZZ-IEEE, Vol. 1, pp.185-193, (1995).

Google Scholar

[2] H. Q Wang and P. Chen. Fuzzy Diagnosis Method for Rotating Machinery in Variable Rotating Speed, IEEE Sensors Journal, Vol. 11, No. 1, p Peng Chen, Toshio Toyota and Zhengjia He, Automated Function Generation of Symptom Parameters and Application to Fault Diagnosis of Machinery under Variable Operating conditions; IEEE Transactions On Systems, NO. 6, pp.775-781, (2001).

DOI: 10.1109/3468.983436

Google Scholar

[3] Zhang, N. L and D. Poole. Exploiting causal independence in Bayesian network inference, journal of Artificial Intelligence Research, Vol. 5, pp.301-328, (1996).

DOI: 10.1613/jair.305

Google Scholar

[4] K. Murphy, A brief introduction to graphical models and Bayesian networks, The Bayes Net Toolbox for Matlab, Computing Science and Statistics, 33, (2001).

Google Scholar

[5] N. Friedman, anM. d Goldszmidt. Learning Bayesian networks with local structure, in Proceedings of the 12th Conference on Uncertainty in Arificial Intelligence, Portland, Augest 1-4 (1996).

Google Scholar