Geometry and invariance in kernel based methods
Advances in kernel methods
Swarm intelligence
Expert Systems with Applications: An International Journal
Forecasting volatility based on wavelet support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy logic-based decision-making for fault diagnosis in a DC motor
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control
Expert Systems with Applications: An International Journal
An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
Applied Soft Computing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Condition monitoring and fault diagnosis of rolling element bearings timely and accurately is very important to ensure the reliable operation of rotating machinery. In this paper, a multi-fault classification model based on the kernel method of support vector machines (SVM) and wavelet frame, wavelet basis were introduced to construct the kernel function of SVM, and wavelet support vector machine (WSVM) is presented. To seek the optimal parameters of WSVM, particle swarm optimization (PSO) is applied to optimize unknown parameters of WSVM. In this work, the vibration signals measured from rolling element bearings are preprocessed using empirical model decomposition (EMD). Moreover, a distance evaluation technique is performed to remove the redundant and irrelevant information and select the salient features for the classification process. Hence, a relatively new hybrid intelligent fault detection and classification method based on EMD, distance evaluation technique and WSVM with PSO is proposed. This method is validated on a rolling element bearing test bench and then applied to the bearing fault diagnosis for electric locomotives. Compared with the commonly used SVM, the WSVM can achieve a greater accuracy. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on the vibration signals.