Bearing Condition Identification Using Improved Multiscale Entropy and SVMs

Long Zhang; Guo Liang Xiong; He Sheng Liu; Hui Jun Zou

doi:10.4028/www.scientific.net/KEM.419-420.817

Paper Titles

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine
p.801

The Order Tracking Study of Variant Speed Process of Aeroengine Vibration Signal
p.805

Multi-Axis EDM CNC System Based on RT-Linux
p.809

Fabrication of Micro Structures by Ultra Short Voltage Pulse Electrochemical Machining
p.813

Bearing Condition Identification Using Improved Multiscale Entropy and SVMs
p.817

Tangent Hole Machined by Micro Electrical Discharge Machining
p.821

Dynamic Stress Concentration of Circular Cavity and Double Linear Cracks in Elastic Medium
p.825

Dynamics Model and Control of Path Following of Wheel Mobile Robot
p.829

Study on Computer-Aided Optimizing Milling Parameters of Numerical Control Machine
p.833

HomeKey Engineering MaterialsKey Engineering Materials Vols. 419-420Bearing Condition Identification Using Improved...

Bearing Condition Identification Using Improved Multiscale Entropy and SVMs

Abstract:

This paper presents a method for bearing health condition identification based on improved multiscale entropy (IMSE) and support vector machines (SVMs). IMSE refers to the calculation of improved sample entropies, i.e., fuzzy sample entropies (FSampEn) across a sequence of time scales, which takes into account not only the nonlinearity but also the interactions and coupling between mechanical components, thus providing much more information regarding machine health condition compared to traditional single scale-based entropy. On the other hand, in engineering practice, the amount of fault samples is often limited, which thus decrease the performance of traditional classifiers like artificial neural networks (ANNs). Currently popular SVMs provide a favorable solution to small sample-sized problems. In this study, IMSE and SVMs were employed as fault feature extractor and classifier, respectively. The experimental results verify that the proposed method has potential applications in bearing health condition identification.