ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Data-model interactive remaining useful life prediction method for multi-sensor monitored linear stochastic degrading devices
Received date: 2022-03-15
Revised date: 2022-05-10
Accepted date: 2022-05-25
Online published: 2022-06-09
Supported by
National Natural Science Foundation of China(62073336)
Advances in sensing and monitoring techniques enable real time acquisition of multi-sensor monitoring data of stochastic degrading devices. The effective fusion of these multi-sensor monitoring data to precisely predict the RUL in the prognostics field has drawn extensive attention. This paper presents a novel data-model interactive RUL prediction method for multi-sensor monitored linear stochastic degrading devices with a random failure threshold. In the offline training part， an optimization objective function synthesizing the mean squared error between the predicted and the actual life as well as the variance of the predicted life is constructed based on the composite health index extracted from historical multi-sensor data and the associated lifetime prediction via stochastic degradation modeling. A closed-loop feedback mechanism is thus established for composite health index constructing and stochastic degradation modeling. Based on this feedback mechanism， the fusion coefficients for multi-sensor data and the distribution parameters of the random failure threshold of the associated composite health index are optimized to enable automatic matching of the constructed composite health index with the adopted stochastic degradation model. In the online prediction part， the composite health index is first constructed based on the multi-sensor data of the device in service and then the linear stochastic degradation model is applied to track the degradation progression. To adapt the degradation model to the current status of the device， the Bayesian method is proposed to update the model parameters， and the RUL distribution considering the randomness of the failure threshold is subsequently derived according to the concept of the first passage time. Finally， we validate the proposed method by the multi-sensor data of aircraft gas turbine engines and the results indicate its merits in improving the properties of the composite health index and the accuracy of the prognosis.
Tianmei LI , Xiaosheng SI , Jianxun ZHANG . Data-model interactive remaining useful life prediction method for multi-sensor monitored linear stochastic degrading devices[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(8) : 227190 -227190 . DOI: 10.7527/S1000-6893.2023.27190
| 1 | 李杰， 李响， 许元铭， 等. 工业人工智能及应用研究现状及展望［J］. 自动化学报， 2020， 46（10）： 2031-2044. |
| LEE J， LI X， XU Y M， et al. Recent advances and prospects in industrial AI and applications［J］. Acta Automatica Sinica， 2020， 46（10）： 2031-2044 （in Chinese）. | |
| 2 | 韩中， 程林， 熊金泉， 等. 大数据结构化与数据驱动的复杂系统维修决策［J］. 自动化学报， 2020， 46（2）： 385-396. |
| HAN Z， CHENG L， XIONG J Q， et al. Complex system maintenance decisions based on big data structuration and data-driven［J］. Acta Automatica Sinica， 2020， 46（2）： 385-396 （in Chinese）. | |
| 3 | 陆宁云， 陈闯， 姜斌， 等. 复杂系统维护策略最新研究进展： 从视情维护到预测性维护［J］. 自动化学报， 2021， 47（1）： 1-17. |
| LU N Y， CHEN C， JIANG B， et al. Latest progress on maintenance strategy of complex system： From condition-based maintenance to predictive maintenance［J］. Acta Automatica Sinica， 2021， 47（1）： 1-17 （in Chinese）. | |
| 4 | 孙长银， 吴国政， 王志衡， 等. 自动化学科面临的挑战［J］. 自动化学报， 2021， 47（2）： 464-474. |
| SUN C Y， WU G Z， WANG Z H， et al. On challenges in automation science and technology［J］. Acta Automatica Sinica， 2021， 47（2）： 464-474 （in Chinese）. | |
| 5 | 袁烨， 张永， 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状［J］. 自动化学报， 2020， 46（10）： 2013-2030. |
| YUAN Y， ZHANG Y， DING H. Research on key technology of industrial artificial intelligence and its application in predictive maintenance［J］. Acta Automatica Sinica， 2020， 46（10）： 2013-2030 （in Chinese）. | |
| 6 | SI X S， WANG W B， HU C H， et al. Remaining useful life estimation - A review on the statistical data driven approaches［J］. European Journal of Operational Research， 2011， 213（1）： 1-14. |
| 7 | 王秀丽， 姜斌， 陆宁云. 基于相关向量机的高速列车牵引系统剩余寿命预测［J］. 自动化学报， 2019， 45（12）： 2303-2311. |
| WANG X L， JIANG B， LU N Y. Relevance vector machine based remaining useful life prediction for traction systems of high-speed trains［J］. Acta Automatica Sinica， 2019， 45（12）： 2303-2311 （in Chinese）. | |
| 8 | SI X S， ZHANG Z X， HU C H. Data-driven remaining useful life prognosis techniques： stochastic models， methods and applications［M］. Berlin， Heidelberg： Springer， 2017. |
| 9 | LEI Y G， LI N P， GUO L， et al. Machinery health prognostics： A systematic review from data acquisition to RUL prediction［J］. Mechanical Systems and Signal Processing， 2018， 104： 799-834. |
| 10 | ZHANG Z X， SI X S， HU C H， et al. Degradation data analysis and remaining useful life estimation： A review on Wiener-process-based methods［J］. European Journal of Operational Research， 2018， 271（3）： 775-796. |
| 11 | HALL D L， LLINAS J. An introduction to multisensor data fusion［J］. Proceedings of the IEEE， 1997， 85（1）： 6-23. |
| 12 | TIAN Z G. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring［J］. Journal of Intelligent Manufacturing， 2012， 23（2）： 227-237. |
| 13 | GAO Y Y， WEN Y X， WU J G. A neural network-based joint prognostic model for data fusion and remaining useful life prediction［J］. IEEE Transactions on Neural Networks and Learning Systems， 2021， 32（1）： 117-127. |
| 14 | AL-DULAIMI A， ZABIHI S， ASIF A， et al. A multimodal and hybrid deep neural network model for Remaining Useful Life estimation［J］. Computers in Industry， 2019， 108： 186-196. |
| 15 | LISTOU ELLEFSEN A， BJ?RLYKHAUG E， ?S?Y V， et al. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture［J］. Reliability Engineering & System Safety， 2019， 183： 240-251. |
| 16 | MIAO H H， LI B， SUN C， et al. Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks［J］. IEEE Transactions on Industrial Informatics， 2019， 15（9）： 5023-5032. |
| 17 | VAN ASSELT M B A， MESMAN J， VAN‘T KLOOSTER S A. Dealing with prognostic uncertainty［J］. Futures， 2007， 39（6）： 669-684. |
| 18 | LI G Y， YANG L， LEE C G， et al. A Bayesian deep learning RUL framework integrating epistemic and aleatoric uncertainties［J］. IEEE Transactions on Industrial Electronics， 2021， 68（9）： 8829-8841. |
| 19 | LIU K B， GEBRAEEL N Z， SHI J J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis［J］. IEEE Transactions on Automation Science and Engineering， 2013， 10（3）： 652-664. |
| 20 | KIM M， SONG C Y， LIU K B. A generic health index approach for multisensor degradation modeling and sensor selection［J］. IEEE Transactions on Automation Science and Engineering， 2019， 16（3）： 1426-1437. |
| 21 | CHEHADE A， SONG C Y， LIU K B， et al. A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes［J］. Journal of Quality Technology， 2018， 50（2）： 150-165. |
| 22 | LI Z， WU J G， YUE X W. A shape-constrained neural data fusion network for health index construction and residual life prediction［J］. IEEE Transactions on Neural Networks and Learning Systems， 2021， 32（11）： 5022-5033. |
| 23 | 彭开香， 皮彦婷， 焦瑞华， 等. 航空发动机的健康指标构建与剩余寿命预测［J］. 控制理论与应用， 2020， 37（4）： 713-720. |
| PENG K X， PI Y T， JIAO R H， et al. Health indicator construction and remaining useful life prediction for aircraft engine［J］. Control Theory & Applications， 2020， 37（4）： 713-720 （in Chinese）. | |
| 24 | PENG K X， JIAO R H， DONG J， et al. A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter［J］. Neurocomputing， 2019， 361： 19-28. |
| 25 | LIU K B， HUANG S. Integration of data fusion methodology and degradation modeling process to improve prognostics［J］. IEEE Transactions on Automation Science and Engineering， 2016， 13（1）： 344-354. |
| 26 | LIU K B， CHEHADE A， SONG C Y. Optimize the signal quality of the composite health ?Index via data fusion for degradation modeling? and prognostic analysis［J］. IEEE Transactions on Automation Science and Engineering， 2017， 14（3）： 1504-1514. |
| 27 | YAN H， LIU K B， ZHANG X， et al. Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions［J］. IEEE Transactions on Reliability， 2016， 65（3）： 1416-1426. |
| 28 | SONG C Y， LIU K B. Statistical degradation modeling and prognostics of multiple sensor signals via data fusion： A composite health index approach［J］. IISE Transactions， 2018， 50（10）： 853-867. |
| 29 | 任子强， 司小胜， 胡昌华， 等. 融合多传感器数据的发动机剩余寿命预测方法［J］. 航空学报， 2019， 40（12）： 223312. |
| REN Z Q， SI X S， HU C H， et al. Remaining useful life prediction method for engine combining multi-sensors data［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（12）： 223312 （in Chinese）. | |
| 30 | 李天梅， 司小胜， 刘翔， 等. 大数据下数模联动的随机退化设备剩余寿命预测技术［J］. 自动化学报， 2022， 48（9）： 2119-2141. |
| LI T M， SI X S， LIU X， et al. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data［J］. Acta Automatica Sinica， 2022， 48（9）： 2119-2141 （in Chinese）. | |
| 31 | SI X S， WANG W B， CHEN M Y， et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution［J］. European Journal of Operational Research， 2013， 226（1）： 53-66. |
| 32 | SI X S， WANG W B， HU C H， et al. Estimating remaining useful life with three-source variability in degradation modeling［J］. IEEE Transactions on Reliability， 2014， 63（1）： 167-190. |
| 33 | Saxena A， Goebel K， Simon D， Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation［C］∥2008 International Conference on Prognostics and Health Management. Piscataway： IEEE Press， 2008： 10423504. |
| 34 | WANG C S， LU N Y， CHENG Y H， et al. A data-driven aero-engine degradation prognostic strategy［J］. IEEE Transactions on Cybernetics， 2021， 51（3）： 1531-1541. |
| 35 | CONOVER W J. Practical nonparametric statistics［M］. 3rd ed. New York： Wiley， 1999. |
| 36 | YU W N， KIM I Y， MECHEFSKE C. Analysis of different RNN autoencoder variants for time series classification and machine prognostics［J］. Mechanical Systems and Signal Processing， 2021， 149： 107322. |
| 37 | PILLAI S， VADAKKEPAT P. Two stage deep learning for prognostics using multi-loss encoder and convolutional composite features［J］. Expert Systems With Applications， 2021， 171： 114569． |
/
| 〈 |
|
〉 |
CJA