伍济钢,文 港,杨 康.改进一维卷积神经网络的航空发动机故障诊断方法[J].电子测量与仪器学报,2023,37(3):179-186 |
改进一维卷积神经网络的航空发动机故障诊断方法 |
Improved one-dimensional convolutional neural network for aero-engine fault diagnosis |
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DOI: |
中文关键词: 一维卷积神经网络 多尺度模块 航空发动机 故障诊断 |
英文关键词:one-dimensional convolutional neural network multi-scale module aero-engine fault diagnosis |
基金项目:国家自然科学基金(51775181)项目资助 |
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中文摘要: |
针对现有航空发动机故障诊断的 1DCNN 方法缺乏故障频率多尺度特征提取能力以及对原始振动信号时域特征提取不
足的问题,通过融合内嵌多尺度层到双通道 1DCNN 提出了改进 1DCNN 的航空发动故障诊断方法。 提出了幅值变化速率的方
法对振动信号进行时域特征增强,在单通道 1DCNN 基础上增加幅值变化通道作为第二通道,构建双通道 1DCNN,加强 1DCNN
的时域特征提取能力,再改进多尺度模块为内嵌多尺度层并应用于 1DCNN 的第一通道,针对航空发动机故障频率域的多尺度
特征进行提取。 最后将改进 1DCNN 应用于航空发动机转静碰摩、叶片断裂等故障的诊断,通过对比实验证明了改进 1DCNN 检
测的优越性、抗噪性、泛化性以及改进点的可行性。 |
英文摘要: |
To address the problems that the existing 1DCNN method for aero-engine fault diagnosis lacks the multi-scale feature
extraction capability of fault frequency and the insufficient extraction of time-domain features of the original vibration signal, improved
1DCNN aero-engine fault diagnosis method is proposed by fusing embedded multiscale layers to dual-channel 1DCNN. The method of
amplitude change rate is proposed for the time domain feature enhancement of vibration signals, and the amplitude change channel is
added as the second channel on the basis of single-channel 1DCNN to build a dual-channel 1DCNN to strengthen the time domain feature
extraction capability of 1DCNN, then the multi-scale module is improved to an embedded multi-scale layer and applied to the first
channel of 1DCNN to extract multi-scale features of aero-engine fault frequency. Finally, the improved 1DCNN is applied to the
diagnosis of aero-engine transient static rubbing, blade fracture and other faults, and the superiority, noise resistance, generalization of
the improved 1DCNN detection and the feasibility of the improvement points are proved through comparative experiments. |
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