Feature selection and condition monitoring of gearbox using SOM

Feature selection is a key issue to pattern recognition and condition monitoring. This paper presents an investigation that uses self-organizing maps network to realize feature selection for gearbox condition monitoring. In order to visualize the trained SOM results more clearly, a novel visualization technique is introduced, which can project the high-dimensional input vectors into a 2-dimensional space and prepare a good basis for further analysis. Then with the use of the responses of every dimensional feature in SOM network neurons weights to the input data evaluated according to the Euclidean distances between them, the feature sets being sensitive to pattern recognition are selected. Gearbox vibration signals measured under different operating conditions are analyzed with the method. The results demonstrate that the method selects sensitive feature sets effectively and has a good potential for gearbox condition monitoring in practice.