| Citation: | XU Dan, XIAO Xiaoqi, FENG Zhixinet al. Remaining life prediction method of mechanical system under uncertain loads[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 376-383. doi: 10.13700/j.bh.1001-5965.2020.0582(in Chinese)
|
Aimed at the problem that the random working load of complex mechanical systems cannot be clearly given, a life prediction method based on hidden semi-Markov model (HSMM) is proposed. After completing the construction of the load space based on the HSMM, the forward and backward transition parameters and the Viterbi algorithm are introduced to solve the model parameters. The estimated parameters are used to predict the transition direction and corresponding probability of random future loads. The prediction result of the load is combined with the life prediction model based on multi-sensor information to predict the remaining life of the system. The effectiveness and correctness of the proposed method are verified by using NASA's commercial modular aero-propulsion system simulation data as a case study.
| [1] |
周绍华, 胡昌华, 司小胜, 等. 运行状态切换下的设备剩余寿命预测[J]. 电光与控制, 2017, 24(2): 95-99. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201702020.htm
ZHOU S H, HU C H, SI X S, et al. Remaining life estimation for products with operation state switching[J]. Electronics Optics & Control, 2017, 24(2): 95-99(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201702020.htm
|
| [2] |
薛小锋, 田晶, 何树铭, 等. 多传感器监测飞机部件非线性退化评估研究[J]. 航空学报, 2021, 42(5): 524342. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202105023.htm
XUE X F, TIAN J, HE S M, et al. Non-linear degradation assessment of aircraft components monitored by multi-sensors[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(5): 524342(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202105023.htm
|
| [3] |
LIU M, YAO X, ZHANG J, et al. Multi-sensor data fusion for remaining useful life prediction of machining tools by IABC-BPNN in dry milling operations[J]. Sensors, 2020, 20(17): 4657. doi: 10.3390/s20174657
|
| [4] |
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): 134-145. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201912012.htm
REN Z Q, SI X S, HU C H, et al. Remaining useful life prediction method for engine combining multi-sensor data[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(12): 134-145(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201912012.htm
|
| [5] |
马奇友, 刘可薇, 杜坚, 等. 基于深度长短期记忆网络的发动机叶片剩余寿命预测[J]. 推进技术, 2021, 42(8): 1888-1897. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJS202108024.htm
MA Q Y, LIU K W, DU J, et al. Prediction of residual life of engine blades based on deep short term memory network[J]. Journal of Propulsion Technology, 2021, 42(8): 1888-1897(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJJS202108024.htm
|
| [6] |
CHEN Y, PENG G, ZHU Z, et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction[J]. Applied Soft Computing, 2020, 86: 105919. doi: 10.1016/j.asoc.2019.105919
|
| [7] |
JAVED K, GOURIVEAU R, ZERHOUNI N. A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering[J]. IEEE Transactions on Cybernetics, 2015, 45(12): 2626-2639. doi: 10.1109/TCYB.2014.2378056
|
| [8] |
YAN H, LIU K, 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. doi: 10.1109/TR.2016.2575449
|
| [9] |
FLORY J A, KHAROUFEH J P, GEBRAEEL N Z. A switching diffusion model for lifetime estimation in randomly varying environments[J]. ⅡE Transaction, 2014, 46(11): 1227-1241. doi: 10.1080/0740817X.2014.893400
|
| [10] |
RAMIN M, ZUO M J. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process[J]. Reliability Engineering & System Safety, 2014, 124: 92-104.
|
| [11] |
FAN L, WANG S P, DUAN H B, et al. Fatigue crack fault diagnosis and prognosis based on hidden semi-Markov model[J]. The Journal of Engineering, 2019, 2019(13): 406-410.
|
| [12] |
DONG M, HE D. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis[J]. European Journal of Operational Research, 2007, 178(3): 858-878.
|
| [13] |
KOUADRI A, HAJJI M, HARKAT M F, et al. Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems[J]. Renewable Energy, 2020, 150: 598-606.
|
| [14] |
XIAO X Q, XU D, FENG Z X. Multi-sensor infusion and data-physics model based remaining useful life prediction[C]//2020 IEEE International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2020: 395-400.
|
| [15] |
SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//2008 International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2008: 1-9.
|
Figures(6) / Tables(9)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
