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田晶,李有儒,艾延廷.一种基于Deep-GBM的航空发动机中介轴承故障诊断方法[J].航空动力学报,2019,(4):756~763
一种基于Deep-GBM的航空发动机中介轴承故障诊断方法
Fault diagnosis of aero-engine inter-shaft bearing based on Deep-GBM
投稿时间:2018-05-28  
DOI:10.13224/j.cnki.jasp.2019.04.003
中文关键词:  故障诊断  中介轴承  样本熵  机器学习  梯度提升模型
英文关键词:fault diagnosis  inter-shaft bearing  sample entropy  machine learning  deep gradent boosting model(Deep-GBM)
基金项目:国家自然科学基金(11702177);辽宁省自然科学基金(20180550650);中央高校基本科研业务费专项基金(2018YJS032)
作者单位
田晶 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室沈阳 110136 
李有儒 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室沈阳 110136北京交通大学 信息科学研究所北京 100044北京交通大学 现代信息科学与网络技术北京市重点实验室北京 100044 
艾延廷 沈阳航空航天大学 辽宁省航空推进系统先进测试技术重点实验室沈阳 110136 
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中文摘要:
      针对航空发动机中介轴承故障信号难于识别的特点,提出了一种深度梯度提升模型(Deep-GBM)对振动信号特征进行逐层学习以提高分类模型的准确率。开展某型航空发动机中介轴承故障模拟实验,并采用经验模式分解(EMD)方法对采集的振动信号进行分解,提取内蕴模式函数(IMF)分量非线性动力学参数样本熵作为原始故障特征。采用Deep-GBM对中介轴承内环故障、内环和滚动体综合故障、正常、滚棒剥落、滚棒划伤五种不同状态进行识别。实验结果表明,所提出的Deep-GBM故障诊断准确率达到87%,相对于传统的机器学习模型准确率最高提升了28%,并具有良好的泛化能力。
英文摘要:
      In view of the difficulty in identifying the fault signal of the inter-shaft bearing of the aero-engine, a deep gradient boasting model (Deep-GBM) was proposed to improve the precision score by learning the feature of vibration signal step by step. Fault simulation experiment was conducted on a type of aeroengine intershaft bearing. Vibration fault signal was decomposed through empirical mode decomposition (EMD) method, and intrinsin mode function (IMF) component sample entropy of nonlinear dynamics parameters was collected as base features. With the model proposed, the aero-engine inter-shaft bearing was diagnosed respectively with fault in inner ring, comprehensive fault in inner ring and rolling element, and in normal condition, with stick-spalled fault and stick-scratched fault. The experimental results showed that the fault diagnosis accuracy of the Deep-GBM reached 87%, 28% higher than that of the traditional machine learning model. Besides, the model has been proved to have good generalization ability.
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