Gear damage diagnosis and classification based on support vector machines

The operation status of gear device can directly affect the working conditions of the whole machine system. Thus, it is crucial to detect the gear damage as early as possible to prevent the system from malfunction. This paper proposes an intelligent diagnosis method for gear damage using multiple classifiers of support vector machines with extracted failure feature vector. The vibration signal of gear box is employed as the analytical data in this paper. In order to illustrate the representative characters of gear conditions, statistical parameters and characteristic amplitude ratios of frequency bands are extracted from the vibration signals in time-domain and frequency-domain respectively, which are served as failure feature vector for the following diagnosis. Moreover, to reduce the dimensions of the failure feature vector, the technology of principal component analysis is adopted to transform the original failure feature vector into a new smaller set of variables as inputs to classifiers of support vector machines. In order to classify different types of gears, multiple classifiers of support vector machines based on the binary tree are designed. The validity of this approach is investigated by the experiment. Three kinds of gears, namely normal gear, spot damaged gear and pitted gear, are tested on the power circulating type gear testing machine. The vibration accelerations of gear box are measured as original data. Most of the samples are correctly classified by the provided method, which demonstrates the effectiveness of the proposed method on the application of gear damage diagnosis.