Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes

Abstract

Deep learning has the ability to mine complex relationships in fault diagnosis. Deep convolutional neural network (DCNN) with deep structures, instead of shallow ones, can be applied to mining useful information from the original vibration data. However, when the number of the training samples is small, the diagnosis accuracy will be affected. As an improvement of the DCNN, deep convolutional neural network based on the Fisher-criterion (FDCNN) can be used for the fault diagnosis of small samples. But the model parameters in the method are based on human labor or prior knowledge, which is bound to bring negative influence on the diagnosis accuracy. Therefore, a novel adaptive Fisher-based deep convolutional neural network (AFDCNN) method, which can optimize the model parameters adaptively, is proposed as an improvement of the FDCNN. Comparative verification test results show that AFDCNN has more outstanding performance.