This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase an...This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase and quadrature(IQ)signals into 2D feature maps,retaining both time and frequency domain features.Compared to residual network 18-layer(ResNet18)and Hilbert transform methods,this approach offers higher accuracy,faster training,and a smaller model size,making it ideal for hardware deployment.展开更多
Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective featu...Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.展开更多
To apply the advantages of deep learning in recognizing two-dimensional(2D)images to three-phase inverter fault diagnosis,a threephase inverter fault diagnosis model based on gramian angular field(GAF)combined with co...To apply the advantages of deep learning in recognizing two-dimensional(2D)images to three-phase inverter fault diagnosis,a threephase inverter fault diagnosis model based on gramian angular field(GAF)combined with convolutional neural network(CNN)was proposed.Since the current signals of the inverter in different working states are different,the images formed by the time series encoding are also different,which enables the image recognition technology to be used for time series classification to identify the fault current signal of the inverter.Firstly,the one-dimensional(1D)inverter fault current signal was converted into a 2D image through the GAF.Next,the CNN model suitable for inverter fault diagnosis was input to realize the detection,classification and location of inverter fault.The simulation results show that the recognition accuracy of this method is 99.36%under different noisy data.Compared with other traditional methods,it has higher accuracy and reliability,and stronger anti-noise interference capability and robustness in dealing with noisy data.Therefore,it is an effective fault diagnosis method for inverters.展开更多
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase and quadrature(IQ)signals into 2D feature maps,retaining both time and frequency domain features.Compared to residual network 18-layer(ResNet18)and Hilbert transform methods,this approach offers higher accuracy,faster training,and a smaller model size,making it ideal for hardware deployment.
基金supported by the Project of Shanghai Engineering Research Center for Intelligent Operation and Maintenance and Energy Efficiency Monitoring of Ships(No.20DZ2252300),China.
文摘Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.
文摘To apply the advantages of deep learning in recognizing two-dimensional(2D)images to three-phase inverter fault diagnosis,a threephase inverter fault diagnosis model based on gramian angular field(GAF)combined with convolutional neural network(CNN)was proposed.Since the current signals of the inverter in different working states are different,the images formed by the time series encoding are also different,which enables the image recognition technology to be used for time series classification to identify the fault current signal of the inverter.Firstly,the one-dimensional(1D)inverter fault current signal was converted into a 2D image through the GAF.Next,the CNN model suitable for inverter fault diagnosis was input to realize the detection,classification and location of inverter fault.The simulation results show that the recognition accuracy of this method is 99.36%under different noisy data.Compared with other traditional methods,it has higher accuracy and reliability,and stronger anti-noise interference capability and robustness in dealing with noisy data.Therefore,it is an effective fault diagnosis method for inverters.
