Sage Journals: Discover world-class research

Abstract

Gear’s vibration signal contains the state information of the gear. Different types of gear faults have different vibration features in the time domain. Fault feature extracted from the vibration feature can be used to diagnose the gear fault. In this paper, the vibration feature of the gear signal in the time domain is analyzed, and the fault feature of the gear is extracted by using the kernel density estimation along with the probability statistical method. Then the vibration signal of the gear is processed and the probability density estimation of the amplitude is obtained through the function of kernel density estimation. Then the probability of the sample point falling to each vibration range is calculated. Finally, the fault diagnosis is achieved by identifying the fault feature based on the fault statistics in various amplitude ranges. According to the experimental results, it is shown that the fault feature extracted in this paper can be applied to the fault diagnosis of a gear.

Keywords

Kernel density estimation gear extract feature fault diagnosis

Get full access to this article

View all access options for this article.

References

Parey

and Pachori

R.B.

, Gear fault diagnosis based on central tendency measure of intrinsic mode functions, International Journal of Comadem 17 (2014), 15–22.

Cheng

J.S.

and Yu

, Application of spectral kurtosis approach based on local mean decomposition (LMD) in gear fault diagnosis, Journal of Vibration and Shock 31 (2012), 20–23, 54.

Zhang

and Xie

, A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis, Measurement 91 (2016), 499–508.

Gan

Yang

et al., Fault diagnosis scheme for open-circuit faults in switched reluctance motor drives using fast Fourier transform algorithm with bus current detection, Let Power Electronics 9 (2016), 20–30.

Rafsanjani

M.K.

and Samareh

, Chaotic time series prediction by artificial neural networks, Journal of Computational Methods in Sciences and Engineering 16 (2016), 1–17.

Yao

and Wang

Z.H.

, Fault diagnosis analysis of gear based on fuzzy fault tree, Advanced Materials Research 204–210 (2011), 1994–1997.

Zhang

J.F.

and Huang

Z.C.

, Discriminant approach to the machinery or sensor fault based on the K-L divergence, Journal of Mechanical Strength 28 (2006), 670–673.

Sun

R.J.

Chen

G.P.

Sun

D.Y.

et al., Probabilistic damage detection based on the kernel density estimation method in aluminum plates, Journal of Vibration, Measurement and Diagnosis 35 (2015), 1054–1062.

Tang

F.X.

and Yang

Y.F.

, Research of color image segmentation algorithm based on asymmetric kernel density estimation, Journal of Computational Methods in Sciences and Engineering 17 (2017), 1–8.

10.

Rosenblatt

, Remarks on some nonparametric estimates of a density function, The Annals of Mathematical Statistics 27 (1956), 832–837.

11.

Parzen

, On estimation of a probability density function and mode, The Annals of Mathematical Statistics 33 (1962), 1065–1076.

12.

Zhang

, Fault Diagnosis and Health Assessment of Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence, MAS Dissertation, University of Electronic Science and Technology of China, 2015.

13.

Wang

Feng

C.L.

Huang

W.X

et al., Non-parametric kernel density estimation of developing species sensitivity distributions, Asian Journal of Ecotoxicology 10 (2015), 215–224.

14.

Guo

Z.Z.

Huo

D.S.

and Sun

Y.F.

, A new bandwidth selection for kernel density estimation, Journal of Jiamusi University (Natural Science Edition) 26 (2008), 401–402.

15.

Y.Q.

Chen

and Nie

, Temporal time dependence model of wind speed series based on non-parametric kernel density estimation method, Acta Energiae Solaris Sinica 38 (2017), 450–455.

16.

Zhou

C.J.

Cheng

and Tang

J.Y.

, Modeling and calculation of impact of mesh-out at corner contact in gear drive, Journal of Aerospace Power 29 (2014), 1205–1210.

17.

Zhou

C.J.

Tang

J.Y.

and Zhong

Z.H.

, Corner contact and impact friction of gear drive, Chinese Journal of Mechanical Engineering 44 (2008), 75–81.

18.

Feng

H.S.

Wang

L.Q.

Zheng

D.Z

et al., Analysis of gear dynamic meshing force considering variable condition impact, Journal of Vibration Measurement and Diagnosis 35 (2015), 212–217.

19.

Calculation of Load Capacity of Spur and Helical Gears – Part 1: Basic Principles, Introduction and General Influence Factors (ISO 6336-1-2006).

20.

Y.B.

Zheng

Zhang

et al., Actuality and prospect of the gear fault diagnosis technology, Machinery Design and Manufacture 2 (2002), 95–97.

The fault diagnosis of gear based on kernel density estimation

Abstract

Keywords

Get full access to this article

References