Gear Faults Diagnosis Based on Nonlinear Blind Source Separation

Xiao Wei Wang; Lin Suo Shi; Shuang Chen; Hui Li; Wei Zhang

doi:10.4028/www.scientific.net/AMR.548.507

Paper Titles

Application of NC Machining Simulation System Based on VERICUT
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A Solution to Weibull Prior Distribution of CNC Life-Span Using Information Entropy Theory
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Prediction of Reliability Model for CNC Machine Tool Based on Exponential Smoothing Model
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A Study on the Electrical Silicon Sheet Punch Wear Machined by W-EDM
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Gear Faults Diagnosis Based on Nonlinear Blind Source Separation
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Study on Engineering Mechanical Properties of Mayanpo Slope at Xiangjiaba Hydropower Station
p.511

Mechanical Failure Analysis of Aircraft Alternator Based on Fault Tree and Case Reasoning
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A New Reliability Prediction Method Based on Physics of Failure Method for Product Design and Manufacture
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Research Progress on Electrostatic Discharge Failure Models in Semiconductor Materials
p.527

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 548Gear Faults Diagnosis Based on Nonlinear Blind...

Gear Faults Diagnosis Based on Nonlinear Blind Source Separation

Abstract:

The nonlinear blind source separation is a practical and effective method in processing mechanical vibration signal, but it has the limitation which learning rate is fixed. It will take a long time for iterative parameters to get convergence. In this paper, a variable rate nonlinear BSS is proposed. The learning rate of the algorithm is adjusted based on iterative error in the different stopping iterating time and inverse proportion. The proposed algorithm increasing the efficiency of the nonlinear BSS and de-noising the vibration signals. Experiment on gears shows that the signal gained by the method more impersonality represents the gear condition

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Periodical:

Advanced Materials Research (Volume 548)

Pages:

507-510

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.548.507

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Online since:

July 2012

Authors:

Xiao Wei Wang, Lin Suo Shi, Shuang Chen, Hui Li, Wei Zhang

Keywords:

Blind Source Separation, Fault Diagnosis, Kernel Function, Nonlinear Mixing, Vibration Signal

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