Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (10): 1937-1947    DOI: 10.3785/j.issn.1008-973X.2021.10.016
机械与能源工程     
基于多时间尺度相似性的涡扇发动机寿命预测
许昱晖(),舒俊清,宋亚,郑宇,夏唐斌*()
上海交通大学 机械系统与振动国家重点实验室，上海 200240
Remaining useful life prediction of turbofan engine based on similarity in multiple time scales
Yu-hui XU(),Jun-qing SHU,Ya SONG,Yu ZHENG,Tang-bin XIA*()
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
 全文: PDF(1382 KB)   HTML
摘要：

针对传统相似性方法在提取健康指标和相似性匹配上存在的不足，提出结合自编码器神经网络的基于多时间尺度健康指标相似性的预测方法（AE MTS-HI）. 采用自编码器从状态监测数据中提取表征发动机退化状态的健康指标，降低提取过程非线性信息的损失. 将测试退化轨迹长度的波动纳入考量，针对性地设计多时间尺度的健康指标进行相似性匹配. 这不仅可以克服单一时间尺度匹配导致的精度限制，而且可以提高预测的鲁棒性. 在涡扇发动机的公开数据集上验证所提方法的性能. 结果表明，利用该方法能够显著提升剩余使用寿命（RUL）的预测精度，为预知维护提供有力支撑.
关键词： 剩余使用寿命（RUL）;  多时间尺度;  自编码器;  相似性方法;  涡扇发动机    
Abstract:

A novel method based on health index similarity in multiple time scales with autoencoder (AE MTS-HI) was proposed aiming at the shortage of the traditional similarity-based method in extracting health index and similarity matching. Autoencoder was applied to construct the health index based on monitoring data, which can minimize the loss of nonlinear information. The health index in multiple time scales was developed for similarity matching by considering the fluctuation of the length of test degradation trajectories. The method can remove the accuracy limitation caused by fixed time scales and enhance the prediction robustness. Performance of the proposed method was evaluated on public turbofan engines datasets. Results demonstrate that the method can improve the remaining useful life (RUL) prediction accuracy and provide stable support for predictive maintenance.
Key words: remaining useful life (RUL)    multiple time scale    autoencoder    similarity-based method    turbofan engine
收稿日期: 2020-11-03 出版日期: 2021-10-27
CLC:  TH 17  
基金资助: 国家自然科学基金资助项目（51875359）；上海市自然科学基金资助项目（20ZR1428600）；上海商用飞机系统工程科创中心联合研究基金资助项目（FASE-2021-M7）；教育部-中国移动联合基金建设项目（MCM20180703）
通讯作者: 夏唐斌     E-mail: xuyuhui@sjtu.edu.cn;xtbxtb@sjtu.edu.cn
作者简介: 许昱晖（1999—），男，硕士生，从事机器学习和装备健康预测的研究. orcid.org/0000-0001-5035-9466. E-mail： xuyuhui@sjtu.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
许昱晖
舒俊清
宋亚
郑宇
夏唐斌

引用本文:

许昱晖,舒俊清,宋亚,郑宇,夏唐斌. 基于多时间尺度相似性的涡扇发动机寿命预测[J]. 浙江大学学报(工学版), 2021, 55(10): 1937-1947.

Yu-hui XU,Jun-qing SHU,Ya SONG,Yu ZHENG,Tang-bin XIA. Remaining useful life prediction of turbofan engine based on similarity in multiple time scales. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1937-1947.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.10.016    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I10/1937

图 1  状态监测参数的示意图
图 2  自编码器的结构
图 3  AE MTS-HI方法的流程图
层 神经元数 激活函数
输入层 $ {d}_{\mathrm{i}\mathrm{n}} $ —
隐含层1 $ {d}_{\mathrm{i}\mathrm{n}}/2 $ ReLU函数
隐含层2 $ {d}_{\mathrm{o}\mathrm{u}\mathrm{t}} $ 线性激活函数
隐含层3 $ {d}_{\mathrm{i}\mathrm{n}}/2 $ ReLU函数
输出层 $ {d}_{\mathrm{i}\mathrm{n}} $ tanh函数
表 1  自编码器的参数设置
图 4  相似性匹配与RUL预测的示意图
数据集 样本个数 数据总量 最小运行周期数 最大运行周期数
训练集 100 20631 128 362
测试集 100 13096 31 303
表 2  FD001数据规模
图 5  FD001样本长度分布
图 6  信号趋势分类
分类 传感器编号
增长型 2，3，4，8，9，11，13，14，15，17
衰减型 7，12，20，21
固定型 1，5，6，10，16，18，19
表 3  传感器信号趋势分类
图 7  RUL标签修正的示意图
测试样本运行长度 测试样本数量 选用时间尺度
$30\leqslant {t}_{i} < 50$ 7 30
$50\leqslant {t}_{i} < 70$ 7 30,50
$70\leqslant {t}_{i}$ 86 30,50,70
表 4  相似性匹配的时间尺度设定
图 8  自编码器参数迭代的实验结果
图 9  退化信息库
图 10  RUL预测结果
方法 RMSE Score
SVM[24] 29.822 —
CNN[9] 18.448 1 286.7
LSTM[10] 16.14 338
Similarity-based[16] 19.87 —
Similarity-based with SVR[25] — 388
Similarity-based with KTST[15] 16.87 377.08
AE MTS-HI 14.07 291.67
表 5  不同预测方法的结果对比
降维方法 RMSE Score
多元线性回归 16.32 486.50
等距特征映射 14.50 339.95
主成分分析 14.29 302.95
自编码器 14.07 291.67
表 6  不同降维方法预测的结果对比
隐含层个数 隐含层神经元数 RMSE
1 1 14.31
3 4，1，4 14.17
3 7，1，7 14.07
3 10，1，10 14.12
5 10，6，1，6，10 14.13
5 7，7，1，7，7 14.09
表 7  不同自编码器结构与参数预测结果的对比
样本长度 样本数量 Score
L=30 L=50 L=70 多时间尺度
$30\leqslant {t}_{i} < 50$ 7 26.30 — — 26.30
$50\leqslant {t}_{i} < 70$ 7 8.41 8.86 — 6.56
$70\leqslant {t}_{i}$ 86 337.70 276.23 284.35 258.81
所有样本 100 372.41 — — 291.67
表 8  多时间尺度对预测结果的影响
图 11  单一时间尺度对预测效果的影响
样本编号 样本长度 RUL e
L=30 L=50 L=70 多时间尺度
23 130 113 4.40 4.99 ?2.77 2.21
60 147 100 4.81 1.61 ?7.17 ?0.25
61 159 21 2.32 5.88 4.69 4.30
76 205 10 0.12 ?1.80 ?1.83 ?1.17
49 303 21 ?2.36 ?3.82 ?4.08 ?3.42
表 9  典例概况及预测误差
图 12  中等长度样本的健康指标
图 13  寿命末期样本的健康指标
1 TAHAN M, TSOUTSANIS E, MUHAMMAD M, et al Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: a review[J]. Applied Energy, 2017, 198: 122- 144
doi: 10.1016/j.apenergy.2017.04.048
2 XIA T, DONG Y, XIAO L, et al Recent advances in prognostics and health management for advanced manufacturing paradigms[J]. Reliability Engineering and System Safety, 2018, 178: 255- 268
doi: 10.1016/j.ress.2018.06.021
3 LIU Y, HU X, ZHANG W Remaining useful life prediction based on health index similarity[J]. Reliability Engineering and System Safety, 2019, 185: 502- 510
doi: 10.1016/j.ress.2019.02.002
4 JAVED K, GOURIVEAU R, ZERHOUNI N State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels[J]. Mechanical Systems and Signal Processing, 2016, 94: 214- 236
5 WANG H, MA X, ZHAO Y An improved Wiener process model with adaptive drift and diffusion for online remaining useful life prediction[J]. Mechanical Systems and Signal Processing, 2019, 127: 370- 387
doi: 10.1016/j.ymssp.2019.03.019
6 王浩伟, 徐廷学, 刘勇 基于随机参数Gamma过程的剩余寿命预测方法[J]. 浙江大学学报:工学版, 2015, 49 (4): 699- 704
WANG Hao-wei, XU Ting-xue, LIU Yong Remaining useful life prediction method based on Gamma processes with random parameters[J]. Journal of Zhejiang University: Engineering Science, 2015, 49 (4): 699- 704
7 ZHANG H, MIAO Q, ZHANG X, et al An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction[J]. Microelectronics Reliability, 2018, 81: 288- 298
doi: 10.1016/j.microrel.2017.12.036
8 CHEN Z, LI Y, XIA T, et al Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy[J]. Reliability Engineering and System Safety, 2019, 184: 123- 136
doi: 10.1016/j.ress.2017.09.002
9 BABU G S, ZHAO P, LI X. Deep convolutional neural network based regression approach for estimation of remaining useful life [C]// Proceedings of the International Conference on Database Systems for Advanced Applications. Berlin: Springer, 2016: 214-228.
10 ZHENG S, RISTOVSKI K, FARAHAT A, et al. Long short-term memory network for remaining useful life estimation [C]// Proceedings of the International Conference on Prognostics and Health Management. Washington, D. C.: IEEE, 2017: 88-95.
11 WANG T, YU J, SIEGEL D, et al. A similarity-based prognostics approach for remaining useful life estimation of engineered systems [C]// International Conference on Prognostics and Health Management. Washington, D. C.: IEEE, 2008: 1-6.
12 ZIO E, DI MAIO F A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system[J]. Reliability Engineering and System Safety, 2010, 95 (1): 49- 57
doi: 10.1016/j.ress.2009.08.001
13 YOU M Y, MENG G A generalized similarity measure for similarity-based residual life prediction[J]. Proceedings of the Institution of Mechanical Engineers, Part E:Journal of Process Mechanical Engineering, 2011, 225 (3): 151- 160
doi: 10.1177/0954408911399832
14 谷梦瑶, 陈友玲, 王新龙 多退化变量下基于实时健康度的相似性寿命预测方法[J]. 计算机集成制造系统, 2017, 23 (2): 362- 372
GU Meng-yao, CHEN You-ling, WANG Xin-long Multi-index modeling for similarity-based residual life estimation based on real-time health degree[J]. Computer Integrated Manufacturing Systems, 2017, 23 (2): 362- 372
15 CAI H, JIA X, FENG J, et al A similarity based methodology for machine prognostics by using kernel two sample test[J]. ISA Transactions, 2020, 103: 112- 121
doi: 10.1016/j.isatra.2020.03.007
16 AZEVEDO D, RIBEIRO B, CARDOSO A. Online simulation of methods to predict the remaining useful lifetime of aircraft components [C]// Proceedings of Experiment International Conference. Washington, D. C.: IEEE, 2019: 199-203.
17 CHARTE D, CHARTE F, GARCÍA S, et al A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines[J]. Information Fusion, 2018, 44: 78- 96
doi: 10.1016/j.inffus.2017.12.007
18 REN L, SUN Y, CUI J, et al Bearing remaining useful life prediction based on deep autoencoder and deep neural networks[J]. Journal of Manufacturing Systems, 2018, 48 (Part C): 71- 77
19 MA J, SU H, ZHAO W, et al Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self-learning[J]. Complexity, 2018, 2018: 1- 13
20 CHEN C, LEU J, PRAKOSA S W. Using autoencoder to facilitate information retention for data dimension reduction [C]// 3rd International Conference on Intelligent Green Building and Smart Grid. Washington, D. C.: IEEE, 2018: 1-5.
21 XIA T, SONG Y, ZHENG Y, et al An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation[J]. Computers in Industry, 2020, 115: 103182
doi: 10.1016/j.compind.2019.103182
22 SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation [C]// International Conference on Prognostics and Health Management. Washington, D. C.: IEEE, 2008: 1-9.
23 HEIMES F O. Recurrent neural networks for remaining useful life estimation [C]// International Conference on Prognostics and Health Management. Washington, D. C.: IEEE, 2008: 1-6.
24 LOUEN C, DING S X, KANDLER C. A new framework for remaining useful life estimation using support vector machine classifier [C]// Proceedings of Conference on Control and Fault-Tolerant Systems. Washington, D. C.: IEEE, 2013: 228-233.
[1] 白文超,韩希先,王金宝. 基于条件生成模型的高效近似查询处理框架[J]. 浙江大学学报(工学版), 2022, 56(5): 995-1005.
[2] 刘兴,余建波. 注意力卷积GRU自编码器及其在工业过程监控的应用[J]. 浙江大学学报(工学版), 2021, 55(9): 1643-1651.
[3] 江金生,任浩然,李瀚野. 基于卷积自编码器的地震数据处理[J]. 浙江大学学报(工学版), 2020, 54(5): 978-984.
[4] 王万良,杨小涵,赵燕伟,高楠,吕闯,张兆娟. 采用卷积自编码器网络的图像增强算法[J]. 浙江大学学报(工学版), 2019, 53(9): 1728-1740.
[5] 何俊昱,肖溪,黄皓旻,施积炎,徐新华. 四岭水库蓝绿藻细胞浓度的多时间尺度分析[J]. 浙江大学学报(工学版), 2016, 50(3): 491-498.