Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery

Shu Qing Zhang; Yu Zhu He; Jin Min Zhang; Yu Chun Zhao

doi:10.4028/www.scientific.net/AMM.130-134.571

Paper Titles

A Generic Model for Spiral Groove Grinding
p.551

Online Calculating Models of Temperature and Thermal Stresses for Turbine Rotors
p.557

A New Feedback Gain Designing of Speed Adaptive Full Order Observer for High Speed Operation of Induction Motor
p.561

Dynamics Analysis of Double-Tamper Mechanism for Asphalt Paver
p.566

Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery
p.571

Dynamic Model of Compression Refrigeration System with a High Pressure Receiver
p.575

Load Rotational Moment and Power of Centrifuges with Rotational Arms
p.580

Modeling and Simulation of NC Precision Sphere Grinding Machine Feed Drive System
p.585

Research on a Novel 3-DOF Planar Parallel Manipulator Driven by Three-Slider and its Larger Workspace Analysis
p.590

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134Multi-Fractal Based Fault Diagnosis Method of...

Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery

Abstract:

Aiming at complex features of the fault rotating machinery such as nonstationary and nonlinearity, a new method for fault diagnosis based on multi-fractal was introduced. The vibration signals firstly are analyzed by multi-fractal theory and have multi-fractal characteristics. Then the area of multi-fractal spectrum S and the entropy of multi-fractal spectrum Hm were extracted as new criterions to diagnose machinery faults. Results of experimental analysis indicate that the method is effective and it provides a new way in fault diagnosis of rotating machinery.

You might also be interested in these eBooks

Mechanical and Electronics Engineering III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

571-574

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.571

Citation:

Cite this paper

Online since:

October 2011

Authors:

Shu Qing Zhang, Yu Zhu He, Jin Min Zhang, Yu Chun Zhao

Keywords:

Area of Multi-Fractal Spectrum, Entropy of Multi-Fractal Spectrum, Fault Diagnosis, Multi-Fractal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Li Yoyrong, ZENG Fali, LV Yong. Application of wavelet packet analysis to gear fault diagnosis[J]. Journal of Vibration and Shock, 2005, 24(5): 101-103. (in Chinese).

Google Scholar

[2] KONG Qingpeng, SONG Kaichen, CHEN Ying. Study of time frequency order tracking of vibration signals in engine speed changing stage[J]. Journal of Vibration Engineering, 2005, 18(4): 448-452. (in Chinese).

Google Scholar

[3] Xu Yuxiu, YUAN peixin, YANG Wenping. Study on condition prediction of automobile engine based onKovmogolov entropy[J]. Journal of Vibration and Shock, 2004, 23(3): 101-106. (in Chinese).

Google Scholar

[4] FAN Fumei, LIANG Ping, WU Gengshen. Experimental research on steam turbine rotor vibration fault diagnosis based on the fractal box counting dimension[J]. Nuclear Power Engineering, 2006, 27(1): 85-89. (in Chinese).

Google Scholar

[5] HE WEnge, ZHOU Jingxin. Application of fractal dimension in fault diagnosis of motor[J]. Journal of Jilin Institute of Chemical Technology, 2005, 22(4): 84-86. (in Chinese).

Google Scholar

[6] Norouzzadeh P，Rahmani B. A multi-fractal detrended fluctuation description of Iranian rial-US dollar exchange rate[J]. Physica A, 2006, 367: 328-336.

DOI: 10.1016/j.physa.2005.11.019

Google Scholar

[7] Telesca L, Lapenna V. Measuring multi-fractality in seismic sequences[J]. Tectonophysics, 2006, 423(1-4): 115-123.

DOI: 10.1016/j.tecto.2006.03.023

Google Scholar

[8] XIE Ping, LIU Bin, WANG Xiao, LIN Hongbin. Multifractal entropy and its application to analysis of non-stational signals[J]. Chinese Journal of Scientific Instrument, 2005, 8(26): 610-612. (in Chinese).

Google Scholar

[9] http: /www. eecs. case. edu/laboratory/bearing/download. htm.

Google Scholar