An Interval-Valued Prediction Method for Remaining Useful Life of Aero Engine

被引:0
作者
Zhang, Bingce [1 ]
Wang, Degang [1 ]
Song, Wenyan [2 ]
Zhang, Shuo [1 ]
Lin, Sida [1 ]
机构
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
[2] Dongbei Univ Finance & Econ, Sch Econ, Dalian 116025, Peoples R China
来源
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE | 2020年
基金
国家重点研发计划;
关键词
Aero engine; remaining useful life; interval information granule;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aero engine is the core power unit of aircraft. A satisfactory prediction of remaining useful life (RtJL) for an aero engine can ensure timely replacement and maintenance of engine and can avoid huge loss caused by engine failure during flight mission. In this paper, a multi-layer pereeptron (MLP) prediction model based on information granular theory is proposed to estimate the RUL interval of aero engine. Firstly, the principal component analysis (PCA) method is considered to reduce dimension of inputs due to large number of sensor measurements of aero engines. Then a method to find the degradation interval of engine by the principle of justifiable granularity is designed, and the expected maximum RUL of each engine can be determined dynamically. Furthermore, a multi-layer perceptron is used to find the relation between aero engine sensor measurements and remaining useful life, by which the RUL interval of aero engine is obtained. Some numerical simulations show that the proposed model can obtain satisfactory performance.
引用
收藏
页码:5790 / 5795
页数:6
相关论文
共 16 条
  • [1] Allan J., 2014, J ENG GAS TURB POWER, V136, P051201
  • [2] Remaining Useful Life Prediction for Aero-Engines Combining Sate Space Model and KF Algorithm
    Cai Jing
    Zhang Li
    Dong Ping
    [J]. TransactionsofNanjingUniversityofAeronauticsandAstronautics, 2017, 34 (03) : 265 - 271
  • [3] Gugulothu N, 2018, INT J PROGN HEALTH M, V9
  • [4] Heimes FO, 2008, 2008 INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (PHM), P59
  • [5] A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering
    Javed, Kamran
    Gouriveau, Rafael
    Zerhouni, Noureddine
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (12) : 2626 - 2639
  • [6] Direct Remaining Useful Life Estimation Based on Support Vector Regression
    Khelif, Racha
    Chebel-Morello, Brigitte
    Malinowski, Simon
    Laajili, Emna
    Fnaiech, Farhat
    Zerhouni, Noureddine
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (03) : 2276 - 2285
  • [7] Laredo D, 2019, NEURAL NETWORKS OFFI, V116
  • [8] Lim P, 2016, IEEE IJCNN, P1746, DOI 10.1109/IJCNN.2016.7727410
  • [9] Litt JonathanS., 2004, J AEROS COMP INF COM, V1, P543, DOI [10.2514/1.13048, DOI 10.2514/1.13048]
  • [10] Designing Fuzzy Sets With the Use of the Parametric Principle of Justifiable Granularity
    Pedrycz, Witold
    Wang, Xianmin
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2016, 24 (02) : 489 - 496