北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (9): 1818-1825.doi: 10.13700/j.bh.1001-5965.2017.0774

• 论文 • 上一篇    下一篇

基于多分类AdaBoost的航空发动机故障诊断

曹惠玲1, 高升1, 薛鹏2   

  1. 1. 中国民航大学 航空工程学院, 天津 300300;
    2. 中国民航大学 工程训练中心, 天津 300300
  • 收稿日期:2017-12-13 出版日期:2018-09-20 发布日期:2018-09-21
  • 通讯作者: 曹惠玲.E-mail:hlcao@cauc.edu.cn E-mail:hlcao@cauc.edu.cn
  • 作者简介:曹惠玲 女,博士,教授,硕士生导师。主要研究方向:航空发动机状态监控、故障诊断与性能分析;高升 男,硕士研究生。主要研究方向:发动机状态监控与故障诊断、数据挖掘、机器学习。
  • 基金资助:
    中央高校基本科研业务费专项资金(3122014D010)

Aeroengine fault diagnosis based on multi-classification AdaBoost

CAO Huiling1, GAO Sheng1, XUE Peng2   

  1. 1. College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China;
    2. Engineering Training Center, Civil Aviation University of China, Tianjin 300300, China
  • Received:2017-12-13 Online:2018-09-20 Published:2018-09-21
  • Supported by:
    the Fundamental Research Funds for the Central Universities (3122014D010)

摘要: 对航空发动机运行数据进行数据挖掘的方法,是发动机故障诊断研究领域的重要研究内容。由于各种算法自身的局限性,通过某种单一算法很难大幅度提升故障分类的准确性。运用组合分类的AdaBoost算法,综合多个分类模型进行诊断,是提升故障识别精度的一种较好的方法。通过AdaBoost算法及其改进算法的结合,建立一种多分类的AdaBoost算法,以支持向量机(SVM)为基础分类器,进行综合诊断模型的建立。通过单位向量法、比值系数法和相关系数法将指印图中统计的故障标识数据进行处理,得到不受故障程度影响的训练数据,再进行建模。实验表明,AdaBoost相关结合算法能够显著提升分类器性能。根据实际故障案例,验证了所建立的诊断模型能够较好地用于发动机的故障诊断。

关键词: AdaBoost, 支持向量机(SVM), 单位向量法, 比值系数法, 相关系数法, 故障诊断

Abstract: The data mining of aeroengine operational data is an important research for engine fault diagnosis. Due to the limitations of various algorithms, the accuracy of fault classification is difficult to be greatly enhanced with a single algorithm. Using a combination of classifications and diagnosis of multiple classification models, AdaBoost algorithm is a good method to improve the fault recognition accuracy. This paper combined the AdaBoost algorithm and its improved algorithm, and established a multi-classification AdaBoost algorithm. Support vector machine (SVM) was taken as the basic classifier, and a comprehensive diagnostic model was established. Fault identification data in statistics of fingerprint maps were processed with unit vector, ratio coefficient and correlation coefficient, and the training data for fault diagnosis with few effects of fault degrees were obtained. Then the model was constructed. The experimental results illustrate that the AdaBoost based combination algorithm can significantly improve the performance of classifier. With the actual fault cases, it is verified that the established diagnostic model can be well applied to engine fault diagnosis.

Key words: AdaBoost, support vector machine (SVM), unit vector, ratio coefficient, correlation coefficient, fault diagnosis

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