计算机应用 ›› 2020, Vol. 40 ›› Issue (12): 3534-3540.DOI: 10.11772/j.issn.1001-9081.2020050661

• 人工智能 • 上一篇    下一篇

基于遗传算法选优的集成手段与时序卷积网络的涡扇发动机剩余寿命预测

朱霖1, 宁芊1, 雷印杰1, 陈炳才2   

  1. 1. 四川大学 电子信息学院, 成都 610065;
    2. 大连理工大学 计算机科学与技术学院, 辽宁 大连 116085
  • 收稿日期:2020-05-18 修回日期:2020-08-01 出版日期:2020-12-10 发布日期:2020-08-26
  • 通讯作者: 宁芊(1969-),女,四川成都人,教授,博士,主要研究方向:模式识别、系统优化。ningq@scu.edu.cn
  • 作者简介:朱霖(1997-),男,安徽池州人,硕士研究生,主要研究方向:设备剩余寿命预测;雷印杰(1983-),男,四川成都人,副教授,博士,主要研究方向:图像融合、图像超分辨率;陈炳才(1976-),男,福建龙岩人,教授,博士,主要研究方向:干扰对齐
  • 基金资助:
    国家自然科学基金资助项目(61771089);新疆自治区区域协同创新专项(科技援疆计划)(2019E0214)。

Remaining useful life prediction for turbofan engines by genetic algorithm-based selective ensembling and temporal convolutional network

ZHU Lin1, NING Qian1, LEI Yinjie1, CHEN Bingcai2   

  1. 1. College of Electronics and Information Engineering, Sichuan University, Chengdu Sichuan 610065, China;
    2. School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116085, China
  • Received:2020-05-18 Revised:2020-08-01 Online:2020-12-10 Published:2020-08-26
  • Supported by:
    This work is partially supported by National Natural Science Foundation of China (61771089), the Special Project for Regional Collaborative Innovation of Xinjiang Autonomous Region (Science and Technology Assistance Plan for Xinjiang) (2019E0214).

摘要: 涡扇发动机作为航空航天领域的核心设备之一,其健康状况决定了航空器能否稳定可靠地运行。而对涡扇发动机的剩余寿命(RUL)进行判断,是设备监测与维护的重要一环。针对涡扇发动机监测过程中存在的工况复杂、监测数据多样、时间跨度长等特点,提出了一种遗传算法优选时序卷积网络(TCN)基模型的集成方法(GASEN-TCN)的涡扇发动机剩余寿命预测模型。首先,利用TCN捕获长跨度下的数据内在关系,从而对RUL作出预测;然后,应用GASEN集成多个独立的TCN,以增强模型的泛化性能;最后,在通用的商用模块化航空推进系统模拟模型(C-MAPSS)数据集上,对所提模型与当下流行的机器学习方法和其他的深度神经网络进行了比较。实验结果表明,在多种不同的运行模式和故障条件下,与流行的双向长短期记忆(Bi-LSTM)网络相比,所提模型都有着更高的预测准确率与更低的预测误差。以FD001数据集为例,在该数据集上所提模型的均方根误差(RMSE)相较Bi-LSTM低17.08%,相对准确率(Accuracy)相较Bi-LSTM高12.16%。所提模型在设备的智能检修与维护方面有着较好的应用前景。

关键词: 数据驱动模型, 剩余寿命预测, 时序卷积网络, 集成方法, 涡扇发动机

Abstract: As the turbofan engine is one of the core equipment in the field of aerospace, its health condition determines whether the aircraft could work stably and reliably. And the prediction of the Remaining Useful Life (RUL) of turbofan engine is an important part of equipment monitoring and maintenance. In view of the characteristics such as complicated operating conditions, diverse monitoring data, and long time span existing in the turbofan engine monitoring process, a remaining useful life prediction model for turbofan engines integrating Genetic Algorithm-based Selective ENsembling (GASEN) and Temporal Convolutional Network (TCN) (GASEN-TCN) was proposed. Firstly, TCN was used to capture the inner relationship between data under long span, so as to predict the RUL. Then, GASEN was applied to ensemble multiple independent TCNs for enhancing the generalization performance of the model. Finally, the proposed model was compared with the popular machine learning methods and other deep neural networks on the general Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Experimental results show that, the proposed model has higher prediction accuracy and lower prediction error than the state-of-the-art Bidirectional Long-Short Term Memory (Bi-LSTM) network under many different operating modes and fault conditions. Taking FD001 dataset as an example:on this dataset, the Root Mean Square Error (RMSE) of the proposed model is 17.08% lower than that of Bi-LSTM, and the relative accuracy (Accuracy) of the proposed model is 12.16% higher than that of Bi-LSTM. It can be seen that the proposed model has considerable application prospect in intelligent overhaul and maintenance of equipment.

Key words: data-driven model, Remaining Useful Life (RUL) prediction, Temporal Convolutional Network (TCN), ensembling method, turbofan engine

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