Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension

The development of non-linear dynamic theory brought a new method for recognising and predicting the complex nonlinear dynamic behaviour. Fractal dimension can quantitatively describe the non-linear behaviour of vibration signal. In the present paper, the capacity dimension, information dimension and correlation dimension are applied to classify various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of each fractal dimension and their combinations are evaluated by using SVMs. Experiments on 10 fault data sets showed that the classification performance of the single fractal dimension is quite poor on most data sets, and for a given data set, each fractal dimension exhibited different classification ability, this indicates that various fractal dimensions contain various fault information. Experiments on different combinations of the fractal dimensions demonstrated that the combination of all these three fractal dimensions gets the highest score, but the classification performance is still poor on some data sets. In order to improve the classification performance of the SVM further, I I time-domain statistical features are introduced to train the SVM together with three fractal dimensions, and the classification performance of the SVM is improved significantly. At the same time, experimental results showed that the classification performance of the SVM trained with I I time-domain statistical features in tandem with three fractal dimensions outperforms that of the SVM trained only with I I time-domain statistical features or with three fractal dimensions. (c) 2006 Elsevier Ltd. All rights reserved.