Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for ...Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensional images.This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process.And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network(CNN),which can automatically accomplish the process of the feature extraction and fault diagnosis.The effect of this method is verified by bearing data.The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed.The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.展开更多
In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a c...In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.展开更多
Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive...Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.展开更多
This paper proposes a neural network to implement the maximum likelihood bearingestimation algorithm in real time.We show both analytically and by simulation that this neuralnetwork is guaranteed to be stable and to p...This paper proposes a neural network to implement the maximum likelihood bearingestimation algorithm in real time.We show both analytically and by simulation that this neuralnetwork is guaranteed to be stable and to provide the maximum likelihood bearing estimationwithin an elapsed time of only a few characteristic time constants of the network.As a result,this proposed neural network is satisfactory for real-time hearing estimation.展开更多
In the process of urban development in China,the vast majority of urban construction is faced with the prominent contradiction between scarce land resources and vigorous construction demand.Moreover,high-density and h...In the process of urban development in China,the vast majority of urban construction is faced with the prominent contradiction between scarce land resources and vigorous construction demand.Moreover,high-density and high-intensity development is ubiquitous.However,the overall development amount of a city is restricted by the bearing capacity of road network to some extent,and there is an upper limit.Based on this,Xingtang County of Shijiazhuang City is taken as the research object,and bearing capacity of road network is selected as research emphasis.With the aid of traffi c planning software TransCAD,simulation and quantitative analysis are conducted,and traffi c demand is forecasted,to analyze impact relationship between land-use planning and traffic planning in regulatory planning.It facilitates later modifi cation and optimization of volume rate in the land development intensity index,thus providing rational basis for programme adjustment,preparation and management of regulatory planning in Xingtang County.展开更多
基金co-supported by the National Natural Science Foundation of China(No.51775452)Fundamental Research Funds for the Central Universities,China(Nos.2682019CX35 and 2018GF02)Planning Project of Science&Technology Department of Sichuan Province,China(No.2019YFG0353).
文摘Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensional images.This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process.And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network(CNN),which can automatically accomplish the process of the feature extraction and fault diagnosis.The effect of this method is verified by bearing data.The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed.The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.
基金This project is supported by National Natural Science Foundation of China (No. 5880203).
文摘In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.
文摘Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.
基金Supported by the National Natural Science Foundation of China
文摘This paper proposes a neural network to implement the maximum likelihood bearingestimation algorithm in real time.We show both analytically and by simulation that this neuralnetwork is guaranteed to be stable and to provide the maximum likelihood bearing estimationwithin an elapsed time of only a few characteristic time constants of the network.As a result,this proposed neural network is satisfactory for real-time hearing estimation.
文摘In the process of urban development in China,the vast majority of urban construction is faced with the prominent contradiction between scarce land resources and vigorous construction demand.Moreover,high-density and high-intensity development is ubiquitous.However,the overall development amount of a city is restricted by the bearing capacity of road network to some extent,and there is an upper limit.Based on this,Xingtang County of Shijiazhuang City is taken as the research object,and bearing capacity of road network is selected as research emphasis.With the aid of traffi c planning software TransCAD,simulation and quantitative analysis are conducted,and traffi c demand is forecasted,to analyze impact relationship between land-use planning and traffic planning in regulatory planning.It facilitates later modifi cation and optimization of volume rate in the land development intensity index,thus providing rational basis for programme adjustment,preparation and management of regulatory planning in Xingtang County.
文摘针对传统滚动轴承故障诊断方法过度依赖人工提取与分析特征、模型泛化性差以及对时序和通道深层次特征读取不充分的问题,提出了一种基于时频图与改进的卷积神经网络(Convolutional Neural Network,CNN)相结合的滚动轴承故障诊断方法。首先,将滚动轴承的原始振动信号经过连续小波变换(Continuous Wavelet Transform,CWT)转化为二维时频图,再利用内嵌长短期记忆网络(Long Short Term Memory,LSTM)的二维卷积神经网络从变换后的时频图中充分提取图像的时序特征,然后,通过高效通道注意力机制(Efficient Channel Attention,ECA)获取通道的全局信息并自适应地对各通道权重值进行动态调整,建立通道间的联系,自适应提取深层次关键特征。最后,利用凯斯西储大学滚动轴承故障数据集进行实验验证。实验结果表明,相较于一些常见的滚动轴承故障诊断方法,该方法在诊断准确率方面有明显提高。
