International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 2, No. 4, 29 Apr. 2012
Cover page and Table of Contents: PDF (size: 334KB)
The Fault Diagnosis Research of Gearbox Based on Hilbert-Huang Transform
Full Text (PDF, 334KB), PP.71-77
Views: 0 Downloads: 0
Author(s)
Cao Fengcai
1,*
Pan Hongxia
1
Index Terms
Gearbox, Condition monitoring, HHT, Energy-proportion spectrum, Fault diagnosis
Abstract
The signal processing based on Hilbert-Huang transform is very suitable for nonlinear and non stationary process; it can extract gear fault features effectively. In this paper we aim at the engineering need of gearbox real-time monitoring and fault diagnosis, expanding a study of JZQ250 Gear Box. We use Hilbert-Huang transform to measure gear vibration signal processing, and use the obtained instantaneous frequency Hilbert marginal spectrum as the fault feature of the gearbox fault diagnosis. Tests showed that, the marginal spectrum based on Hilbert-Huang transform can get the characteristics of fault signal frequency of the gearbox, thus it can identify the type of gearbox fault effectively and achieve early fault prediction. The characteristics of instantaneous frequency can describe the corresponding fault type better. It has a good prospect in the field of gearbox fault diagnosis.
Cite This Paper
Cao Fengcai,Pan Hongxia,"The Fault Diagnosis Research of Gearbox Based on Hilbert-Huang Transform", IJEME, vol.2, no.4, pp.71-77, 2012. DOI: 10.5815/ijeme.2012.04.12
Reference
[1]S R Long, N E Huang. On the Normalized Hilbert Transform and Its Applications in Remote Sensing. Signal And Image Processing for Remote Sensing. New York: CRC Press New York. 2006:3-22.
[2]Yu Dejie, Cheng Junsheng, Yang Yu. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2005,19(2): 259-270. (In Chinese)
[3]B. Liu, S. Riemenschneider, Y. Xu. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mechanical Systems and Signal Processing. 2006(20):718-734.
[4]Hao Yunhu. The application of wavelet transform in gearbox fault diagnosis. Taiyuan: Master thesis of NUC, 2009
[5]Huang N E,Shen Z, Long S R. The Empirical Mode Composition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis [J]. Proc R Soc London, 1998, 454: 903-995.
[6]Shorey A, Kordonski W, Tricard M. The application of Hilbert-Huang transform energy spectrum in bearing fault diagnosis [J]. Journal of Ordnance Engineering College2005, 17(4):37-40
[7]Xiang Ling,Zhu Yongli,Tang Guiji. The application of HHT in the Rotor Fault Diagnosis[J]. Proceedings of the CSEE, 2007, 27(35): 84-88. (In Chinese)
[8]Huang N E, Shen Z, Long S R. A New View of Non- linear Waves: The Hilbert Spectrum[J]. Annual Review Fluid Mechanics, 1999, 31(5): 417-457.
[9]Y.Tani and K. Kawata. The theory and application of the machinery’s non-stationary signal diagnosis [M].Beijing: Higher Education Press, 2001
[10]Willian Kordonski. Rub-impact fault diagnosis of the rotor systems based on EMD[J].Mechanism and Machine Theory,2009,44:784-791.