北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (3): 621-628.doi: 10.13700/j.bh.1001-5965.2017.0197

• 论文 • 上一篇    下一篇

基于深度学习的航空发动机故障融合诊断

车畅畅, 王华伟, 倪晓梅, 洪骥宇   

  1. 南京航空航天大学民航学院, 南京 210016
  • 收稿日期:2017-04-05 出版日期:2018-03-20 发布日期:2018-03-30
  • 通讯作者: 王华伟 E-mail:wang_hw66@163.com
  • 作者简介:车畅畅,男,硕士研究生。主要研究方向:可靠性、维修性及维修工程;王华伟,女,博士,教授,博士生导师。主要研究方向:民航安全工程、民航维修工程、可靠性工程。
  • 基金资助:
    国家自然科学基金(71401073);民航联合研究基金(U1233115)

Fault fusion diagnosis of aero-engine based on deep learning

CHE Changchang, WANG Huawei, NI Xiaomei, HONG Jiyu   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2017-04-05 Online:2018-03-20 Published:2018-03-30
  • Supported by:
    National Natural Science Foundation of China (71401073); Joint Research Foundation for Civil Aviation (U1233115)

摘要: 通过对航空发动机故障诊断,能够正确判断各部件工作状态,快速确定维修方案,保证飞行安全。在结合深度信念网络和决策融合算法的基础上,提出了基于深度学习的航空发动机故障融合诊断模型。该模型通过分析发动机的大量性能参数,先利用深度学习模型提取出性能参数中的隐藏特征,得出故障分类置信度;其后对多次故障分类结果进行决策融合,从而得出更准确的诊断结果。将普惠JT9D发动机故障系数用于数据仿真,通过算例验证本文算法的有效性;算例计算结果表明:多次实验结果经数据融合提高了可信度,该模型具有较高的故障分类诊断准确性和抗干扰能力。

关键词: 深度学习, 故障诊断, 决策融合, 抗干扰能力, 航空发动机

Abstract: Through the fault diagnosis of aero-engine, the working status of each component can be correctly judged, and the maintenance program can be determined quickly to ensure the safety of flight. Based on the combination of deep belief network and decision fusion theory, the fault fusion diagnosis model of aero-engine based on deep learning was proposed. This model, through analyzing a large number of engine performance parameters, starts with getting fault classification confidence via hidden features in engine performance parameters extracted by deep learning algorithm, and then the multiple fault classification results were fused by decision fusion method to get more accurate results. The JT9D engine failure coefficient was simulated as data to prove the validity of the method. The results of an example show that the reliability of the data has been improved by fault fusion diagnosis of several experimental results, and the model has high fault classification and diagnosis accuracy and anti-interference ability.

Key words: deep learning, fault diagnosis, decision fusion, anti-interference ability, aero-engine

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