The nature of statistical learning theory
The nature of statistical learning theory
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Classification of surface EMG signal using relative wavelet packet energy
Computer Methods and Programs in Biomedicine
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Fault diagnosis of ball bearings using continuous wavelet transform
Applied Soft Computing
Expert Systems with Applications: An International Journal
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Pathological infant cry analysis using wavelet packet transform and probabilistic neural network
Expert Systems with Applications: An International Journal
Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
Digital Signal Processing
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In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.