Fault Diagnosis of Ball Bearing Using Dynamic Convolutional Neural Networks Under Varying Speed Condition

Abstract

The driving speed of bearing in rotating machines is usually variable rather than constant, so methods for accurate fault diagnosis under varying speed condition is required. In this paper, we propose the fault diagnosis model using dynamic convolutional neural network (DY-CNN) that considers variation of fault frequency characteristics by utilizing content-adaptive kernels for fault diagnosis of bearing under varying speed condition. As the input of model, 1-second intervals of vibration data with varying speed condition were used. These kernels adapt to short interval vibration data with varying speed condition by applying weighted sum of trained basis kernels. DY-CNN-based fault diagnosis model improved diagnosis accuracy by 7.07% compared to CNN-based fault diagnosis model. In addition, we showed that the adaptive kernels changed depending on fault types. DY-CNN-based fault diagnosis model adapted itself to fault types, and it performed accurate and robust fault diagnosis of ball bearing under varying speed condition.