1.北京信息科技大学机电工程学院北京100192;2.高端装备制造智能感知与控制北京市国际科技合作基地 北京100192;3.北京信息科技大学机电系统测控北京市重点实验室北京100192
TN07;TK477
北京市自然科学基金重点暨北京市教委重点项目（21JC0016）、国家自然科学基金（51975058）项目资助
Qiao Qi
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, ChinaWang Hongjun
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, ChinaMa Kang
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, ChinaWang Zheng
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, ChinaYu Chenglong
1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, China1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.Beijing Highend Equipment Intelligent Perception and Control International Cooperation Base, Beijing 100192, China; 3.Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science &Technology University, Beijing 100192, China
实际工业环境中，燃气轮机转子故障数据难以采集导致故障样本稀缺，无法满足故障模型的海量训练要求。利用DenseNet在图像特征提取方面的和Transformer结构在视觉领域上的优势，提出了一种基于改进的DenseNet-ViT联合网络的燃气轮机转子故障诊断方法。首先舍弃掉DenseNet的分类层，只需利用DenseNet的特征提取层，随后将改进的DenseNet的输出层连接到ViT模型的输入层构成联合网络；另外针对故障模型训练耗时长的问题，利用迁移学习将训练好模型权重参数进行迁移可以加快训练时间，节省计算资源。利用在实验室构建的燃气轮机转子模拟实验台可以获得燃气轮机转子故障模拟数据，在某型号燃气轮机试车台上获得了真实环境下的转子不同类型的故障数据，利用模拟数据与真实数据进行模型测试可以更好的检验所提出方法的可靠性。实验结果表明：在两种不同转子故障数据集测试中分别达到了96.8%和97.3%的故障识别准确率，表明该方法具有较高的转子故障识别精度；在后续设置的对比验证实验中，通过与CNN以及VGG-16等进行对比，该模型的故障分类准确率也均高于这些网络，从而进一步验证了该模型的优异性和可靠性。
In the actual industrial environment, the collection of gas turbine rotor fault data is challenging, leading to a scarcity of fault samples and an inability to meet the massive training requirements of fault models. Leveraging the advantages of DenseNet in image feature extraction and the Transformer structure in the visual field, an improved gas turbine rotor fault diagnosis method based on the DenseNet-ViT joint network was proposed. Firstly, the classification layer of DenseNet was abandoned, and only the feature extraction layer of DenseNet was utilized. Subsequently, the output layer of the modified DenseNet was connected to the input layer of the ViT model to form the joint network. Additionally, in response to the issue of lengthy training time for the fault model, transfer learning was employed to transfer the trained model’s weight parameters, which could expedite the training process and conserve computing resources. Simulated data of gas turbine rotor faults could be acquired through the gas turbine rotor simulation experimental platform constructed in the laboratory, and real fault data of different types of rotors in the actual environment were obtained on a certain type of gas turbine test bed. Utilizing both the simulated and real data for model testing could better verify the reliability of the proposed method. The experimental results indicate that the fault recognition accuracy rates reached 96.8% and 97.3% in the tests of two distinct rotor fault datasets, respectively, demonstrating that this method possesses a relatively high rotor fault recognition accuracy. In the subsequent comparative verification experiments, by comparing with CNN and VGG-16, etc, the fault classification accuracy of this model was also higher than those networks, thereby further validating the superiority and reliability of this model.
乔琦,王红军,马康,王正,余成龙.基于改进的DenseNet-VIT联合网络和迁移学习的燃气轮机转子故障诊断[J].电子测量与仪器学报,2024,38(11):40-47
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