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

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。

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.