A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model

Gang Yu; Jian Kang

doi:10.4028/www.scientific.net/AMR.977.349

Paper Titles

A Study of Remote Monitoring Fire Alarm System Based on Internet of Things
p.330

Combined Test of Capacity Regulating in 1100kV Controlled Shunt Reactor Valve-Control System
p.334

Adaptive Fuzzy Sliding Mode Control for Brushless Double-Fed Wind Power Generation System
p.338

In-Vehicle Temperature Regulating System Based on Theory Calculation
p.344

A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model
p.349

Study on Water Engineering with Hydromechanics and the Bubble Float Flow Measuring Method
p.353

Numerical Design of Pillar Width in the Inclined Coal Seam
p.361

Multi-Objective Optimal Design of a Double Circular Gear Based on the Differential Evolution Algorithm
p.365

Design of Wireless Sensor Gateway Based on ARM
p.370

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 977A New Method for Multi-Fault Diagnosis of Rotating...

A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model

Abstract:

As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 977)

Pages:

349-352

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.977.349

Citation:

Cite this paper

Online since:

June 2014

Authors:

Gang Yu*, Jian Kang

Keywords:

Mixture Alpha Stable Distribution, Multi-Fault, Rotating Machinery, SOM

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] D. Dyer and R.M. Stewart, Detection of rolling element bearing damage by statistical vibration analysis. Journal of mechanical design, 100. 2(1978): 229-235.

DOI: 10.1115/1.3453905

Google Scholar

[2] F.A. Andrade, I. Esat and M.N.M. Badi, Gear condition monitoring by a new application of the Kolmogorov-Smirnov test. Proceedings of the Institution of Mechanical Engineers. Journal of Mechanical Engineering Science, 215. 6(2001): 653-661.

DOI: 10.1243/0954406011524027

Google Scholar

[3] D. Salas-Gonzalez, E.E. Kuruoglu and D.P. Ruiz, Finite mixture of alpha-stable distributions. Digital Signal Processing, 19. 2(2009): 250-264.

DOI: 10.1016/j.dsp.2007.11.004

Google Scholar

[4] J.M. Chambers, C.L. Mallows and B.W. Stuck, A method for simulating stable random variables. Journal of the American Statistical Association, 71. 354(1976): 340-344.

DOI: 10.1080/01621459.1976.10480344

Google Scholar

[5] J. Antoni and R.B. Randall, Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm. Mechanical Systems and Signal Processing, 18. 1(2004): 103-117.

DOI: 10.1016/s0888-3270(03)00013-x

Google Scholar

[6] R. Huang, L. Xi and X. Li, Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, 21. 1(2007): 193-207.

DOI: 10.1016/j.ymssp.2005.11.008

Google Scholar