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
针对轴承微小故障信号非平稳非线性且易受背景噪声干扰的特点,提出了一种基于格拉姆角场和多尺度卷积神经网络(Gramian angular field and multi-scale convolutional neural network,GAF-MCNN)的智能故障诊断方法。首先,利用分段聚合...针对轴承微小故障信号非平稳非线性且易受背景噪声干扰的特点,提出了一种基于格拉姆角场和多尺度卷积神经网络(Gramian angular field and multi-scale convolutional neural network,GAF-MCNN)的智能故障诊断方法。首先,利用分段聚合近似算法对原始振动信号进行压缩降维预处理,以减少数据存储空间和提升计算效率;然后,利用格拉姆角场算法将一维序列信号转换为二维矩阵热图,二维化后的矩阵加强了原始振动信号间的时间关系,将时间维度编码到了矩阵结构中;最后,设计了基于多尺度卷积神经网络对故障进行高效快速智能诊断。实验结果表明,GAF-MCNN诊断方法不仅克服了传统卷积神经网络诊断方法存在的计算效率较低的问题,而且诊断准确率优于单尺度卷积神经网络方法,具有较强的工程实用性。展开更多
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.
文摘针对轴承微小故障信号非平稳非线性且易受背景噪声干扰的特点,提出了一种基于格拉姆角场和多尺度卷积神经网络(Gramian angular field and multi-scale convolutional neural network,GAF-MCNN)的智能故障诊断方法。首先,利用分段聚合近似算法对原始振动信号进行压缩降维预处理,以减少数据存储空间和提升计算效率;然后,利用格拉姆角场算法将一维序列信号转换为二维矩阵热图,二维化后的矩阵加强了原始振动信号间的时间关系,将时间维度编码到了矩阵结构中;最后,设计了基于多尺度卷积神经网络对故障进行高效快速智能诊断。实验结果表明,GAF-MCNN诊断方法不仅克服了传统卷积神经网络诊断方法存在的计算效率较低的问题,而且诊断准确率优于单尺度卷积神经网络方法,具有较强的工程实用性。
