| 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 |
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| 引用本文： | 朱沁玥,何海昊,李锋,李泽东,李志农,谷士鹏,程娟.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[J].失效分析与预防,2022,17(1):1-8. |
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| 作者姓名： | 朱沁玥 何海昊 李锋 李泽东 李志农 谷士鹏 程娟 |
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| 作者单位： | 1.无损检测技术教育部重点实验室（南昌航空大学），南昌 330063 |
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| 基金项目： | 江西省自然科学基金;国家自然科学基金;航空科学基金 |
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| 摘 要： | 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...
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| 关 键 词： | 齿轮箱 故障诊断 短时傅里叶变换 卷积神经网络 |
| 收稿时间： | 2021-09-20 |
A Fault Diagnosis Method of Helicopter Gearbox Based on Short-Time Fourier Transform and Deep Convolutional Neural Network |
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| ZHU Qin-yue,HE Hai-hao,LI Feng,LI Ze-dong,LI Zhi-nong,GU Shi-peng,CHENG Juan.A Fault Diagnosis Method of Helicopter Gearbox Based on Short-Time Fourier Transform and Deep Convolutional Neural Network[J].Failure Analysis and Prevention,2022,17(1):1-8. |
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| Authors: | ZHU Qin-yue HE Hai-hao LI Feng LI Ze-dong LI Zhi-nong GU Shi-peng CHENG Juan |
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| Abstract: | 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%. |
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