Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine

Xiang Tao Yu; Wen Xiu Lu; Fu Lei Chu

doi:10.4028/www.scientific.net/KEM.413-414.607

Paper Titles

Wavelet Selection for Bearing Defect Diagnosis
p.575

Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis
p.583

Shaft Crack Analysis and Diagnosis for the 600MW Steam Turbine Generator
p.591

Experimental Investigation of Pedestal Looseness in a Rotor-Bearing System
p.599

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine
p.607

Signal Processing by Energy Normalization Method Based on Wavelet Packet
p.613

Application of Cyclic Correlation Analysis to Gear Damage Detection
p.621

Feature Extraction for Damage Detection in Structures Based on Nonlinearity Analysis
p.627

Diagnosis of Rolling Elements Bearing Based on Inverse Autoregressive Filter
p.635

HomeKey Engineering MaterialsKey Engineering Materials Vols. 413-414Fault Diagnostics Based on Pattern Spectrum...

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine

Abstract:

Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.