Research on a Method of Fault Signal Extraction Based on Improved Algorithm

Ying Wang; You Rong Li; Xiao Qin Zhu; Pan Lin; Yue Sheng Luo

doi:10.4028/www.scientific.net/AMM.217-219.2692

Paper Titles

Noise Character of Cooling Tower and Control Discussion
p.2674

The Evaluation about Vibratory Mixing Methods
p.2678

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network
p.2683

Study on Chip-Breaking Technology for Numerical-Control Varying-Feed Drilling of Deep Hole
p.2688

Research on a Method of Fault Signal Extraction Based on Improved Algorithm
p.2692

The Error Analysis and Detection of A/C Axes Bi-Rotary Milling Head
p.2697

Prediction of Top Coal Caving Ability Based on Support Vector Machine
p.2702

Adaptive Interpolation of Tool Posture Vector in Five-Axis Flank Milling
p.2706

Effect of Structure Parameters on Dynamic Characteristics of Piezoelectric Vibrator in Ultrasonic EDM Processing
p.2711

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 217-219Research on a Method of Fault Signal Extraction...

Research on a Method of Fault Signal Extraction Based on Improved Algorithm

Abstract:

Considering the difficulty of diagnosis signal de-noising and feature extraction problems, according to the characteristics of periodicity and shock attenuation respond of mechanical fault vibration signals, a method of improved sequential decomposition algorithm is proposed, it transforms an initial time series into a group of two-dimensional time series, prominent time series partial information, time series decomposition is reversible, can be used for filtering and feature extraction of time signal. Through the simulation and experiments, the validity of method for highlighting partial feature information of the signal is verified, helping to extract weak fault information in strong background noise environment.

You might also be interested in these eBooks

Advanced Materials and Process Technology

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 217-219)

Pages:

2692-2696

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.217-219.2692

Citation:

Cite this paper

Online since:

November 2012

Authors:

Ying Wang, You Rong Li, Xiao Qin Zhu, Pan Lin, Yue Sheng Luo

Keywords:

Diagnosis Signal, Feature Extraction and Separation, Signal Processing, Vibration Signal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Temujin G, Danilo P, Mar c M. The delay vector variance method for determinism nonlinearity in time series [ J] . Physical D, 2004, ( 190) : 167-176.

Google Scholar

[2] LV Y,LI Y R,WANG Z G.A method of weak fault characteristic signal extraction and its application research[ J ].Journal of Vibration Engineering, 2007, 20 (1) : 24 - 28.

Google Scholar

[3] CAI J E, ZHAO W L, Application of Stochastic Resonance in Weak Signal Detection [J]. Journal of Hangzhou electronic technology university, 2005, 25 (1): 26 - 29.

Google Scholar

[4] ZHANG W, XIANG B R. A New Single Well Potential Stochastic Resonance Algorithm to Detect the Weak Signal [ J ].Talanta, 2006, 70: 267 - 271.

DOI: 10.1016/j.talanta.2006.02.039

Google Scholar

[5] Cai Z J, ZENG L J, FENG Z J. Extracting the Weak Distorted Fringes on the Dragonfly Wing by a Correlation Algorithm. Op tics & Laser Technology, 2001, 33: 493- 497.

DOI: 10.1016/s0030-3992(01)00067-6

Google Scholar

[6] LIU J, WU J, LOU Z G, et al. Stochastic Resonance Enhancing Delectability of Weak Signal by Neuronal Networks Model for Receiver [M ]. ISNN, 2006, LNCS 3971, 2006: 15- 20.

DOI: 10.1007/11759966_3

Google Scholar