Combined Improved EEMD with SVM in the Application of Intellgent Fault Diagnosis

Mei Jun Zhang; Hao Chen; Jie Huang; Kai Chai

doi:10.4028/www.scientific.net/AMR.706-708.1774

Paper Titles

Applications of DC Transformer in DC Distributed Generations System
p.1755

Band-Pass Digital Filter Window Function Method Design and Spectrum Leak Analysis Based on MATLAB
p.1759

Bearing Fault Diagnosis Based on Wavelet Analysis
p.1763

Calculation of Follower Displacement Error for a Translating Roller-Follower Plate Cam
p.1769

Combined Improved EEMD with SVM in the Application of Intellgent Fault Diagnosis
p.1774

Design and Analysis of Bicycle Helmet with Impaxx Foam Liner
p.1778

Harmonic Responses of Blades with Piezoelectric or Piezomagnetic Coatings
p.1782

Ka-Band Satellite Channel Multi-State Markov Model Based on Dust Storm Weather and Shadow Multi-Path Effects
p.1786

Loose Detecting of Tower Crane Bolts Based on Time Series Analysis
p.1790

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 706-708Combined Improved EEMD with SVM in the Application...

Combined Improved EEMD with SVM in the Application of Intellgent Fault Diagnosis

Abstract:

Intelligent diagnosis is the development direction of mechahnical condition monitoring and fault diagnosis.Conbined improved EEMD with SVM in fault intelligent diagnosis is researched in this paper.To bearing normal and fault as an example,impove EEMD decomposed 9D normalized energy for characteristic vector applied to the binary classification and identification.Compared to the SVM classification accuracy using different kernel functions that is linear,polynomial,RBF and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear and polynomial kernel function is a hundred percent.Bearing normal and fault two kinds of state is completely correct apart. And the normal and fault state of the binary classification and identification using RBF and Sigmoid kernel function appeared wtong points.