Abstract:
PURPOSE : This work under consideration makes use of support vector machines (SVM) for regression and genetic algorithms (GA) which may be referred to as SVMGA, to classify faults in low-speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions.
METHODS : GA is used as a heuristic tool in finding profound solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and hence without the forehand knowledge of the probabilistic distribution can form new features from the original dataset. Using SVM for regression, the non-linear regression and fault recognition are achieved. Classification is performed for three classes. In this work, the GA is used to first optimize the statistical features for the best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speed, regardless of the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions. RESULTS : This study accomplished the effective classification of different bearing fault patterns especially at low speeds and at varying load conditions using support vector machines (SVM) for regression and genetic algorithms (GA) referred to as SVMGA.
