唐竞鹏,王红军,钟建琳,刘淑聪,张翔,徐伍峰.基于WDCNN SVM深度迁移学习的燃气轮机转子故障诊断方法*[J].电子测量与仪器学报,2021,35(11):115-123
基于WDCNN SVM深度迁移学习的燃气轮机转子故障诊断方法*
Gas turbine rotor fault diagnosis method based on WDCNN SVM deep transfer learning
  
DOI:
中文关键词:  燃气轮机转子  振动分析  迁移学习  故障诊断
英文关键词:gas turbine rotor system  vibration analysis  transfer learning  fault diagnosis
基金项目:国家自然科学基金(51975058)项目资助
作者单位
唐竞鹏 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; 
王红军 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; 
钟建琳 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; 
刘淑聪 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; 
张翔 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; 
徐伍峰 南京航空航天大学航空学院南京210016 
AuthorInstitution
Tang Jingpeng 1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China 
Wang Hongjun 1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China 
Zhong Jianlin 1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China 
Liu Shucong 1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China 
Zhang Xiang 1.School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,Beijing 100192, China; 2.Intelligent Perception and Control of Highend Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China 
Xu Wufeng College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China 
摘要点击次数: 525
全文下载次数: 0
中文摘要:
      针对在使用深度学习对燃气轮机转子故障诊断过程中,因振动信号样本中正常运行数据多、故障数据少而使得模型故障诊断准确率低的问题,提出了一种采用深度迁移学习对燃气轮机转子进行故障诊断的方法。首先,使用典型行业样本数据集预训练第一层宽卷积核深度卷积神经网络(WDCNN)模型,给予模型初始的权重。其次,在源域中,使用某型燃气轮机试车获得的大量正常运行样本更新WDCNN模型的权重;在目标域中,利用源域训练的卷积层提取燃气轮机的正常和故障数据样本特征,然后使用支持向量机(support vector machines, SVM)进行分类识别,从而达到燃气轮机故障识别的目的。试车数据实验结果表明,该方法能够实现96%的识别准确率,验证了将轴承数据集预训练的深度学习模型迁移到燃气轮机转子领域进行故障诊断的可行性。
英文摘要:
      In industries for gas turbine rotor, there are a large number of normal operation vibration signal sample data and few fault data,which caused fault diagnosis accuracy lower. A gas turbine rotor deep transfer learning fault diagnosis method is proposed. First, a first layer wide convolutional kernel deep convolutional neural network (WDCNN) model is pre trained with a typical industry sample dataset, obtained the model initial weights. Second, in the source domain, the weights of the WDCNN model are updated using a large number of normal operation samples obtained from the test drive of a certain type of gas turbine; In the target domain, the normal and fault data sample characteristics of the gas turbine are extracted by using the convolutional layer trained in the source domain, and then the support vector machines (SVM) are used for classification identification, so as to achieve the gas turbine fault identification. The experimental results of the test data show that the method identification accuracy is 96%, which verifies the feasibility of migrating the pre trained deep learning model of the bearing dataset to the field of gas turbine rotor for fault diagnosis.
查看全文  查看/发表评论  下载PDF阅读器