| Rolling bearing is an important part of rotating machinery,and its health status directly determine the working efficiency and security performance of the equipment.Due to the running environment,complex and changeable conditions,such as impact load,frequent faults of rolling bearing,so the state of rolling bearing fault monitoring and diagnosis is of great significance.Taking rolling bearings as the research object,this paper studies and discusses the key problems existing in the current fault diagnosis field,and proposes four kinds of rolling bearing fault diagnosis methods based on the category of deep convolutional neural network by combining with deep learning theory.The research content of the paper is summarized as follows:(1)This paper introduces the research background and significance of this topic.The structure,fault types and vibration signal characteristics of rolling bearing are analyzed.The development process of rolling bearing fault diagnosis is summarized and studied,which is divided into three stages,and the common diagnosis methods in each stage are summarized and analyzed.Aiming at the key problems in current fault diagnosis methods,different solutions are proposed.(2)To deal with the problems that the traditional fault diagnosis methods rely on artificial prior knowledge and network model based on the deep learning method need to set many parameters,a fault diagnosis method of rolling bearing based on sensitive component and MCPG is put forward.Firstly,the vibration signal is processed by empirical mode decomposition.Then the discrete Fréchet distance is used as the measurement index to select the fault sensitive component as the data source to represent the fault state.Finally,the training and test of the network model of multi convolutional pool group(MCPG)are completed by using this data source.Experiments prove that this method can very accurately completed for each type of fault identification.(3)In order to solve the problem that deep learning-based fault diagnosis method is difficult to achieve accurate recognition in small training samples,a fault diagnosis method of rolling bearing based on temporal-spatial feature is proposed.The model has three main functional layers,which can take into account the temporal and spatial characteristics of input data.The experimental results show that the method can achieve better recognition effect and generalization ability on small training sample problem.(4)Aiming at the problem that the deep learning method cannot realize efficient and accurate identification due to the single model structure and incomplete input features,a rolling bearing fault diagnosis method based on dual stream CPG network architecture was proposed.The proposed model has two feature learning routes,which can take into account the features of various types of sample data,and the number of routes and feature types can be adjusted according to different needs.The experimental results show that the method has good recognition effect,generalization ability and universality,and has a better performance in small sample problems.(5)In view of the problems of deep structure of traditional convolutional neural network,complex adjustment of network parameters and the limited classifier capability,a fault diagnosis method based on convolutional neural network and sparrow search algorithm optimized SVM is proposed.The method can effectively solve the shortcomings of the traditional convolutional neural network,such as the too many output layer parameters and the insufficient classification ability of the Softmax classifier.Besides it can adaptively adjust the parameters of SVM,and make the classifier has better classification ability.The experimental results show that the method can gain higher recognition accuracy,and has good universality.(6)Based on the previous research,the rolling bearing fault data analysis and intelligent diagnosis platform is developed.It can read and analyze the collected data,train and test the model. |
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