Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

Zi Fa Li; Jin Guo Li

doi:10.4028/www.scientific.net/AMR.199-200.927

Paper Titles

Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension
p.905

A Method of Signal-Noise Separation from Measured Vibration Response Signals
p.909

Estimation of Random Sonic Fatigue Life Based on Peak Probability Density of Von Mises Stress
p.913

Amplitude Incremental Complementary Energy Principle for Nonlinear Vibration of Elastic System
p.922

Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM
p.927

Rolling Element Bearing Fault Detection Using Redundant Second Generation Wavelet Packet Transform
p.931

Vibration Analysis and Optimal Design for Cab’s Isolation System of Vibratory Roller
p.936

An Optimization Approach of a Seeker's Optomechanical System under Random Vibration by Using Integrated Structural/Optical Numerical Simulation
p.941

Dynamic Analysis for a Subsurface Elastic Cylindrical Inclusion with a Semi- Cylindrical Hill Impacted by SH-Wave
p.945

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 199-200Multichannel Vibration Fault Diagnosis for Rolling...

Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

Abstract:

A new method had been proposed in this paper of fault diagnosis for rolling bearings based on multichannel vibration signals and QPCA-SVM-based method. The vibration signals were obtained by some multi-sensors with three channels X, Y, Z, that were orthogonal axes. The three orthogonal axes signals were constructed a pure quaternion sequences as samples for processing. The pure quaternion sequences data set was processed by quaternion principle components analysis (QPCA) for feature extraction, and then combined with pattern recognition tools support vector machine (SVM) for classifying some faults patterns. The experimental results indicated its efficiency, and it provided a method for fault diagnosis on multichannel vibration signals.