基于超统计的多阶段航空发动机剩余寿命预测

刘君强; 胡东斌; 潘春露; 雷凡; 赵倩茹

doi:10.13700/j.bh.1001-5965.2019.0647

基于超统计的多阶段航空发动机剩余寿命预测

doi: 10.13700/j.bh.1001-5965.2019.0647

1.
南京航空航天大学民航学院, 南京 211000
2.
南京航空航天大学外国语学院, 南京 211000

基金项目:

国家自然科学基金 U1533128

南京航空航天大学研究生创新基地(实验室)开放基金 kfjj20190716

中央高校基本科研业务费专项资金 NS2020050

详细信息

作者简介:
刘君强男, 博士, 副教授。主要研究方向:交通信息工程及控制、交通运输规划与管理

胡东斌男, 硕士研究生。主要研究方向:交通信息工程及控制

通讯作者:
刘君强, E-mail: liujunqiang@nuaa.edu.cn

中图分类号: V267
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-26
- 录用日期: 2020-04-19
- 网络出版日期: 2021-01-20

Remaining useful life prediction of multi-stage aero-engine based on super statistics

1.
Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211000, China
2.
Foreign Language College, Nanjing University of Aeronautics and Astronautics, Nanjing 211000, China

Funds:

National Natural Science Foundation of China U1533128

The Open Funds of Nanjing University of Aeronautics and Astronautics kfjj20190716

the Fundamental Research Funds for the Central Universities NS2020050

More Information

Corresponding author: LIU Junqiang, E-mail:liujunqiang@nuaa.edu.cn

摘要

摘要:
针对传统航空发动机剩余寿命（RUL）预测模型无法客观描述多阶段性能衰退过程及对于RUL预测精度不高的问题, 提出了一个新的多阶段航空发动机RUL预测模型, 包括超统计理论、突变点检测、无迹卡尔曼滤波（UKF）与非线性预测4部分内容。提出了基于超统计理论的多阶段分割滤波（BS-MSF）算法。首先, 该算法采用超统计理论进行突变点检测, 将航空发动机的健康数据划分为多个退化阶段；其次, 应用UKF对融合的时变参数进行滤波处理；最后, 通过非线性拟合对发动机RUL进行预测, 实验采用美国NASA发布的航空发动机数据进行数据分析和验证。结果表明：所提算法在发动机性能退化中的预测具有更好的适应性和更小的拟合误差, 能更准确地预测发动机的RUL, 预测精度比单阶段方法提高5.5%。
- 超统计 /
- 多阶段 /
- 无迹卡尔曼滤波(UKF) /
- 航空发动机 /
- 剩余寿命(RUL) /
- 非线性
Abstract:
Traditional aero-engine Remaining Useful Life (RUL) model cannot objectively describe the multi-stage degeneration process, and the accuracy of RUL prediction is low. To solve this problem, a new multi-stage RUL prediction model for RUL prediction is proposed, including super statistics theory, mutation point detection, Unscented Kalman Filter (UKF) and nonlinear prediction. In the paper, a Multi-stage Segmentation Filtering based on Super statistics (BS-MSF) theory algorithm is proposed. In this algorithm, first, super statistics theory is used to conduct mutation point detection and divide the health data of aero-engine into multiple degradation phases. Then, UKF is used to filter the fused time-varying parameters. Finally, the real RUL of the aero-engine is estimated by nonlinear fitting. nonlinear fitting, and the aero-engine data was released by National Aeronautics and Space Administration. Simulation results show that the presented method has better adaptability in predicting engine performance degradation, smaller fitting error, and more accurate prediction of RUL. The prediction accuracy is 5.5% higher than that of single-stage method.
- super statistics /
- multi-stage /
- Unscented Kalman Filter (UKF) /
- aero-engine /
- Remaining Useful Life (RUL) /
- nonlinear

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图 1 BS-MSF算法

Figure 1. BS-MSF algorithm

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图 2 归一化的监测序列

Figure 2. Normalized monitoring sequence

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图 3 T24的突变点与退化量

Figure 3. Mutation point and degradation quantity of T24

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图 4 多阶段非线性融合HI序列

Figure 4. Multi-stage nonlinear fusion of HI sequence

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图 5 滤波结果比较

Figure 5. Comparison of filtering results

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图 6 寿命演化过程

Figure 6. Life evolution process

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表 1 用于发动机RUL预测的重要监测参数

Table 1. Major monitoring parameters for prediction of engine RUL

符号	含义	单位
T24	低压压气机出口总温	℃
T30	高压压气机出口总温	℃
T50	低压涡轮出口总温	℃
P30	高压压气机出口总压	kPa
Nf	风扇转子转速	r/min
Nc	核心转子转速	r/min
T48	高压涡轮出口总温	℃

下载: 导出CSV

表 2 T24突变点的显著性

Table 2. Significance of mutation point of T24

分割点	1	2	3	4	5
显著性	1	1	1	1	0.982

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表 3 各特征参数的对应突变点

Table 3. Corresponding mutation point of each characteristic parameter

特征参数	突变点1	突变点2	突变点3	突变点4
T24	141	192	219	252
T30	86	173	215	270
T50	122	191	218	263
P30	122	182	215	245
Nf	142	191	228	243
Nc	131	209	234	268
T48	104	181	218	253

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表 4 拟合误差

Table 4. Fitting errors

方法	寿命演化过程
方法	0.5	0.6	0.7	0.8	0.9	1.0
单阶段KF	45	-25	21	11	-7	8
多阶段KF	35	-20	9	19	-5	3
多阶段UKF	17	-10	11	15	-2	-1

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表 5 预测误差

Table 5. Prediction errors

实验次数	单阶段KF	多阶段KF	多阶段UKF
1	0.227	0.161	0.153
2	0.173	0.131	0.145
3	0.191	0.158	0.136
4	0.189	0.167	0.131
5	0.182	0.143	0.121
均值	0.192	0.152	0.137

下载: 导出CSV

参考文献(20)

[1]	ALFORD L D.The problem with aviation COTS[J].IEEE Aerospace and Electronic Systems Magazine, 2001, 16(2):33-37. doi: 10.1109/62.904242
[2]	李军, 杨旭.航空发动机健康管理系统功能架构[J].航空动力学报, 2019(1):71-74. https://www.cnki.com.cn/Article/CJFDTOTAL-HKDL201901019.htm LI J, YANG X.Functional framework of aero engine health management system[J].Journal of Aerospace Power, 2019(1):71-74(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKDL201901019.htm
[3]	郑金涛, 邓四二, 张文虎, 等.航空发动机主轴滚子轴承非典型失效机理[J].航空学报, 2019, 41(5):305-317. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202005022.htm ZHENG J T, DENG S E, ZHANG W H, et al.A typical failure mechanism of aeroengine spindle roller bearing[J].Acta Aeronauticaet Astronautica Sinica, 2019, 41(5):305-317(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202005022.htm
[4]	GARCÍA NIETO P J, GARCÍA-GONZALO E, SÁNCHEZ LASHERAS F, et al.Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[J].Reliability Engineering and System Safety, 2015, 138:219-231. doi: 10.1016/j.ress.2015.02.001
[5]	CAVARZERE A, VENTURINI M.Application of forecasting methodologies to predict gas turbine behavior over time[C]//ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition, 2011.
[6]	任淑红, 左洪福.基于性能衰退的航空发动机剩余寿命组合预测方法[J].机械科学与技术, 2011, 30(1):23-29. https://www.cnki.com.cn/Article/CJFDTOTAL-JXKX201101006.htm REN S H, ZUO H F.A Combined prediction method for the residual-life of civil aviation engines based on performance degradation[J].Mechanical Science and Technology for Aerospace Engineering, 2011, 30(1):23-29(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JXKX201101006.htm
[7]	赵广社, 吴思思, 荣海军.多源统计数据驱动的航空发动机剩余寿命预测方法[J].西安交通大学学报, 2017, 51(11):150-155, 172. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201711021.htm ZHAO G S, WU S S, RONG H J.A multi-source statistics data-driven method for remaining for remaining useful life prediction of aircraft engine[J].Journal of Xi'an Jiaotong University, 2017, 51(11):150-155, 172(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201711021.htm
[8]	张马兰.基于空地数据链的航空发动机剩余寿命预测研究[D].南京: 南京航空航天大学, 2015: 38-48. ZHANG M L.Research on remaining useful life prognostics for aero-engine based on ACARS[D].Nanjing: Nanjing University of Aeronautics and Astronautics, 2015: 38-48(in Chinese).
[9]	RAMIN M, MING J Z.An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process[J].Reliability Engineering and System Safety, 2014, 124(C):92-104. http://www.sciencedirect.com/science/article/pii/S0951832013003037
[10]	BARALDI P, MANGILI F, ZIO E.A Kalman filter-based ensemble approach with application to turbine creep prognostics[J].IEEE Transactions on Reliability, 2012, 61(4):966-977. doi: 10.1109/TR.2012.2221037
[11]	AN D, CHOI J H, KIM N H.Prognostics 101:A tutorial for particle filter-based prognostics algorithm using Matlab[J].Reliability Engineering and System Safety, 2013, 115(1):161-169. http://www.sciencedirect.com/science/article/pii/S0951832013000549
[12]	张马兰.基于分阶段非线性融合的粒子滤波预测算法[J].武汉理工大学学报(交通科学与工程版), 2016, 40(6):1106-1110. doi: 10.3963/j.issn.2095-3844.2016.06.032 ZHANG M L.Particle filter prognostics algorithm based on multi-phase and non-linear fusion[J].Journal of Wuhan University of Technology (Transportation Science and Engineering), 2016, 40(6):1106-1110(in Chinese). doi: 10.3963/j.issn.2095-3844.2016.06.032
[13]	黄亮, 刘君强, 贡英杰.基于Wiener过程的发动机多阶段剩余寿命预测[J].北京航空航天大学学报, 2018, 44(5):1081-1087. doi: 10.13700/j.bh.1001-5965.2017.0383 HUAGN L, LIU J Q, GONG Y J.Multi-phase residual life prediction of engines based on Wiener process[J].Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5):1081-1087(in Chinese). doi: 10.13700/j.bh.1001-5965.2017.0383
[14]	BECK C, COHEN E G D.Superstatistics[J].Physica A:Statistical Mechanics and its Applications, 2003, 322:267-275. doi: 10.1016/S0378-4371(03)00019-0
[15]	李天梅, 邱静, 刘冠军.基于故障率的测试性验证试验故障样本分配方案[J].航空学报, 2009, 30(9):1661-1665. doi: 10.3321/j.issn:1000-6893.2009.09.017 LI T M, QIU J, LIU G J.Allocation plan of failure samples based on failure rate in testability[J].Acta Aeronautica et Astronautica Sinica, 2009, 30(9):1661-1665(in Chinese). doi: 10.3321/j.issn:1000-6893.2009.09.017
[16]	JULIER S J.The spherical simplex unscented transformation[C]//Proceedings of the IEEE American Control Conference.Piscataway: IEEE Press, 2003, 3: 2430-2433.
[17]	黄小平, 王岩.卡尔曼滤波原理及应用:MATLAB仿真[M].北京:电子工业出版社, 2015:103-107. HUANG X P, WANG Y.Kalman filter principle and application:MATLAB simulation[M].Beijing:Electronic Industry Press, 2015:103-107(in Chinese).
[18]	邱恺, 黄国荣, 陈天如, 等.卡尔曼滤波过程的稳定性研究[J].系统工程与电子技术, 2005, 27(1):33-35. doi: 10.3321/j.issn:1001-506X.2005.01.010 QIU K, HUANG G R, CHEN T R, et al.Study of the stability of Kalman filtering process[J].Journal of Systems Engineering and Electronics, 2005, 27(1):33-35(in Chinese). doi: 10.3321/j.issn:1001-506X.2005.01.010
[19]	陈晶, 李波, 高智, 等.航空发动机气路系统故障机理分析[J].长春工业大学学报, 2017, 38(4):402-409. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGX201704015.htm CHEN J, LI B, GAO Z, et al.Fault mechanism analysis of aero-engine gas-path system[J].Journal of Changchun University, 2017, 38(4):402-409(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGX201704015.htm
[20]	熊耀刚.基于贝叶斯网络的可靠性概率风险评价方法研究[D].赣州: 江西理工大学, 2013: 45-49. XIONG Y G.Reliability probability risk evaluation method Based on bayesian network[D].Ganzhou: Jiangxi University of Science and Technology, 2013: 45-49(in Chinese).