| 基于CWT-CNN的齿轮箱运行故障状态识别 |
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| 引用本文： | 梁睿君,冉文丰,余传粮,陈蔚芳,倪德.基于CWT-CNN的齿轮箱运行故障状态识别[J].航空动力学报,2021,36(12):2465-2473. |
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| 作者姓名： | 梁睿君 冉文丰 余传粮 陈蔚芳 倪德 |
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| 作者单位： | 南京航空航天大学机电学院,南京210016;中国航空发动机集团有限公司湖南动力机械研究所,湖南株洲412002 |
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| 基金项目： | 国家重点研发计划（2018YFB2001500）； 国家自然科学基金（51575272） |
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| 摘 要： | 针对传统故障诊断中提取的特征不具有自适应能力、很难匹配特定故障的问题，提出了一种基于连续小波变换（CWT）和二维卷积神经网络（CNN）的齿轮箱故障诊断方法。该方法对齿轮箱故障振动信号采用连续小波变换构造其时频图，以其为输入构建卷积神经网络模型，通过多层卷积池化形成深层分布式故障特征表达。利用反向传播算法调整网络各层的结构参数，使模型建立从信号特征到故障状态之间的准确映射。在不同工况和不同故障状态下的实验中，故障识别准确率达到了99.2%，验证了方法有效性。采用这种自适应学习信号中丰富的信息的方法，可以为故障诊断智能化提供基础。
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| 关 键 词： | 齿轮箱 连续小波变换(CWT) 时频图 故障诊断 卷积神经网络(CNN) |
| 收稿时间： | 2021/8/13 0:00:00 |
Recognition of gearbox operation fault state based on CWT-CNN |
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| LIANG Ruijun,RAN Wenfeng,YU Chuanliang,CHEN Weifang,NI De.Recognition of gearbox operation fault state based on CWT-CNN[J].Journal of Aerospace Power,2021,36(12):2465-2473. |
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| Authors: | LIANG Ruijun RAN Wenfeng YU Chuanliang CHEN Weifang NI De |
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| Affiliation: | 1.College of Mechanical and Electrical Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China2.Hunan Aviation Powerplant Research Institute，Aero Engine Corporation of China，Zhuzhou Hunan 412002，China |
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| Abstract: | In view of the problem that the features extracted by the traditional fault diagnosis do not have adaptive ability and are difficult to match specific faults，a method for fault detection of gearbox based on the continuous wavelet transform （CWT） and two-dimensional convolutional neural network （CNN） was proposed.This method constructed the time-frequency diagrams to raw vibration signals through CWT，then built the CNN model using the diagrams as input，and finally formed a deep distributed fault feature expression through the multiple convolutions and pooling operations.The back propagation algorithm was used to adjust the structural parameters of each layer of the network，making the model establish an accurate mapping from the signal characteristics to the fault states.In experiments under different working conditions and fault states，the fault recognition accuracy reached 99.2%，which verified the effectiveness of the proposed method.Using this method of adaptive learning the abundant information in the signal can provide a basis for the intelligent fault diagnosis. |
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