基于短时傅里叶变换和深度卷积神经网络的直升机齿轮箱故障诊断方法

A Fault Diagnosis Method of Helicopter Gearbox Based on Short-Time Fourier Transform and Deep Convolutional Neural Network

齿轮箱作为直升机重要的传动机构,其运转的可靠性对保障直升机系统安全具有重要的作用。针对传统信号处理需要大量专家经验来识别故障类型的不便性和复杂性,为了实现直升机齿轮箱故障诊断,本研究提出一种基于短时傅里叶变换和深度卷积神经网络的故障诊断方法。首先,将采集到的直升机齿轮箱振动信号利用短时傅里叶变换绘制时频图,以提取振动信号的时频特征;然后,利用深度卷积神经网络学习的前向传播和反向传播对不同故障类别的故障时频图进行训练,以建立不同类别与特征之间的深层联系;最后,训练好的模型可以完成对齿轮箱的故障诊断。结果表明,所提方法能够准确地识别齿轮箱的不同故障类型,准确率超过99%。

Gear box,as an important transmission mechanism of helicopter,its operation reliability plays an important role in ensuring the safety of the helicopter system.A lot of expert experience is required to identify the fault classification in the traditional signal processing method,this traditional identification method brings great inconvenience and complexity to fault diagnosis.Based on this above deficiency,a fault diagnosis method of helicopter gearbox based on Short-time Fourier Transform and deep convolutional neural network is proposed.Firstly,the collected vibration signal of the helicopter gearbox is used to extract the time-frequency characteristics of the vibration signal by utilizing the short-time Fourier transform.Afterwards,the forward propagation and back propagation in the deep convolutional neural network are used to train the time-frequency maps of different faults,in order to establish the relationships between different faults and fault features.Then the constructed model is employed to perform the fault diagnosis of the gearbox.The experimental results show that the proposed method can accurately identify different fault of the gearbox with an accuracy rate of over 99%.