[show abstract]
[hide abstract]
ABSTRACT: Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get
good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been
shown to be particularly successful in many fields such as image identification and face recognition. It also provides us
with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of
rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the
bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial
Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based
on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the
multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on
line diagnosis for mechanical system.
Journal of Zhejiang University - Science A: Applied Physics & Engineering 04/2012; 6(5):433-439. · 0.41 Impact Factor
