Application of Improved MobileNet Network in Bearing Lightweight Diagnosis
In recent years,fault diagnosis methods based on neural networks have shown great advantages in the accuracy and efficiency of diagnosis.However,the exponentially increasing number of model parameters limits the application of neural networks in engineering practice.Aiming at this problem,this paper proposes an improved MobileNet network based on one-dimensional convolutional neural network for fault diagnosis of rolling bearings.The improved network can be directly applied to one-dimensional vibration signals,effectively reducing the requirements of system hardware resources and realizing lightweight deployment of the network;The proposed method is validated using the Western Reserve University bearing dataset and the QPZZ-Ⅱ fault simulation test bench dataset.The accuracy of the model proposed in this paper is more than 99.8%,and the number of parameters is 1/2 of the standard convolutional neural network.The method proposed provides a new way for realizing intelligent diagnosis in light-resource embedded systems.
rolling bearingfault diagnosisneural networkMobileNet
国家科技期刊平台
NSTL
万方数据
维普
