Timely and effective fault diagnosis of sensors is crucial to enhance the working efficiency and reliability of the aeroengine. A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. Firstly, raw fault signals are converted into spectral entropy images by introducing pattern gradient spectral entropy (PGSE), which is used as the input of CNN, because of the great advantage of CNN in processing images and the simple and rapid calculation of the modal gradient spectral entropy. The simulation results prove that IPGSE has more stable distinguishing characteristics. Then, we improved PGSE to use particle swarm optimization algorithm to adaptively optimize the influencing parameters (scale factor ), so that the obtained spectral entropy graph can better match the CNN. Finally, CNN mode is proposed to classify the spectral entropy diagram. The method is validated with datasets containing different fault types. The experimental results show that this method can be easily applied to the online automatic fault diagnosis of aeroengine control system sensors.

中文翻译：

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基于改进模式梯度谱熵的航空发动机传感器智能故障诊断

对传感器进行及时有效的故障诊断对于提高航空发动机的工作效率和可靠性至关重要。针对CNN直接处理一维时间时故障诊断效果差，实时性差的问题，提出一种结合改进的模式梯度谱熵（IPGSE）和卷积神经网络（CNN）的智能故障诊断方案。航空发动机的系列信号。首先，通过引入模式梯度谱熵（PGSE）将原始故障信号转换为谱熵图像，PGSE用作CNN的输入，这是因为CNN在处理图像时具有很大的优势，并且模态梯度的计算简单而快速光谱熵。仿真结果表明，IPGSE具有更稳定的识别特性。然后，），以便获得的光谱熵图可以更好地匹配CNN。最后，提出了CNN模式对频谱熵图进行分类。已使用包含不同故障类型的数据集验证了该方法。实验结果表明，该方法可轻松应用于航空发动机控制系统传感器在线自动故障诊断。