Feature Selection and Classification of Gear Faults Using SOM

Abstract

Feature selection is a key issue to machine fault diagnosis. This paper presents a study that uses self-organizing maps to realize feature selection and reduce dimensionality of the raw feature space for machine faults classification. By means of evaluating the responses of every dimensional feature in SOM networks neurons weights to the input data, the feature sets having the main responses and being sensitive to pattern recognition are selected. Industrial gearbox vibration signals measured under different operating conditions are analyzed using the method. The experimental results indicate that the method selects sensitive feature sets effectively for gear faults classification and recognition, and has a good potential for application in practice.