ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Damage state evaluation method of service turbine blades based on MAML-LSTM
Received date: 2023-09-06
Revised date: 2023-09-19
Accepted date: 2024-01-05
Online published: 2024-02-23
Supported by
National Natural Science Foundation of China(5210050392)
Accurately evaluating the damage state of turbine blades is of great significance for guiding their overhaul / replacement behavior. However， due to the complexity of turbine blade structure and service environment， it is difficult for existing technical means to simulate the blade damage under real service conditions， and the damage prediction based on the damage data of real service blades has the objective limitation of high data acquisition cost and few samples. Therefore， a damage parameter prediction method based on meta-learning is proposed to evaluate the damage state of turbine blades in service under small sample conditions. Based on the limited service data， the damage parameters of turbine blades are predicted. Firstly， the slice samples of different blade heights of turbine blades were prepared， and the microscopic images of typical parts of the slice samples were obtained by field emission scanning electron microscopy. The image processing technology was used to extract the damage data of different parts， and the damage data were divided into different training task data according to the different parts of the picture. Then， a prediction method of turbine blade damage parameters based on MAML-LSTM is proposed. Meta-learning is carried out on the basis of different training tasks， which enhances the correlation between service conditions and blade damage parameters， establishes an effective mapping between service parameters and damage parameters， and realizes the prediction of turbine blade damage parameters. Finally， the MAML-LSTM model proposed in this paper is used to predict the test set data. The average absolute percentage error of the prediction results is 7.55%. Compared with the prediction results of BP， RNN， LSTM， Bi-LSTM and other neural networks， the average absolute error of the model proposed in this paper on the test set is reduced by at least 52.37%， and the mean square error is reduced by at least 76.98%.
Weiqing HUANG , Ning LI , Kailin LIU , Zhizhong FU , Pengfei JI , Shengliang ZHANG , Liwei DONG , Yantao SUN . Damage state evaluation method of service turbine blades based on MAML-LSTM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(18) : 429547 -429547 . DOI: 10.7527/S1000-6893.2024.29547
| 1 | 冯强， 童锦艳， 郑运荣， 等. 燃气涡轮叶片的服役损伤与修复［J］. 中国材料进展， 2012， 31（12）： 21-34. |
| FENG Q， TONG J Y， ZHENG Y R， et al. Service induced degradation and rejuvenation of gas turbine blades［J］. Materials China， 2012， 31（12）： 21-34 （in Chinese）. | |
| 2 | LI D W， LIU J X， SUN Y T， et al. Microstructure and mechanical degradation of K403 Ni-based superalloy from ultra-long-term serviced turbine blade［J］. Journal of Alloys and Compounds， 2023， 957： 170378. |
| 3 | 陈操， 韩雷， 张钰， 等. 镍基合金涡轮叶片的服役损伤机理与性能衰减［J］. 航空材料学报， 2021， 41（4）： 96-108. |
| CHEN C， HAN L， ZHANG Y， et al. Service damage mechanism and performance attenuation of nickel-based alloy turbine blades［J］. Journal of Aeronautical Materials， 2021， 41（4）： 96-108 （in Chinese）. | |
| 4 | ACHARYA M V， FUCHS G E. The effect of long-term thermal exposures on the microstructure and properties of CMSX-10 single crystal Ni-base superalloys［J］. Materials Science and Engineering： A， 2004， 381（1-2）： 143-153. |
| 5 | HAN L， LI P， YU S， et al. Creep/fatigue accelerated failure of Ni-based superalloy turbine blade： Microscopic characteristics and void migration mechanism［J］. International Journal of Fatigue， 2022， 154： 106558. |
| 6 | 范永升， 杨晓光， 石多奇， 等. 服役涡轮叶片筏化判废：定量表征及阈值确定［J］. 航空学报， 2022， 43（9）： 625100. |
| FAN Y S， YANG X G， SHI D Q， et al. Rafting-waste judgement od serviced turbine blades： quantitative characterization and threshold determination［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（9）： 625100 （in Chinese）. | |
| 7 | ELLIS B， HEYNS P S， SCHMIDT S. A hybrid framework for remaining useful life estimation of turbomachine rotor blades［J］. Mechanical Systems and Signal Processing， 2022， 170： 108805. |
| 8 | 雷世英， 孙见忠， 刘赫. 涡轮叶片累积损伤指数模型及服役可靠性评估［J］. 航空学报， 2022， 43（3）： 225064. |
| LEI S Y， SUN J Z， LIU H. Cumulative damage index model and service reliability evaluation of turbine blade［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（3）： 225064 （in Chinese）. | |
| 9 | WANG J， ZHANG J， ZHAO W， et al. Lifetime prediction of turbine blade of turbocharger for diesel engine based on fatigue and creep damage model［C］∥2021 Global Reliability and Prognostics and Health Management. 2021： 1-5. |
| 10 | 车畅畅， 王华伟， 倪晓梅， 等. 基于1D-CNN和Bi-LSTM的航空发动机剩余寿命预测［J］. 机械工程学报， 2021， 57（14）： 304-312. |
| CHE C C， WANG H W， NI X M， et al. Residual life prediction of aeroengine based on 1D-CNN and Bi-LSTM［J］. Journal of Mechanical Engineering， 2021， 57（14）： 304-312 （in Chinese）. | |
| 11 | QUINTANAR-GAGO D A， NELSON P F， DíAZ-SáNCHEZ á， et al. Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network［J］. Reliability Engineering & System Safety， 2021， 207： 107329. |
| 12 | QATTAN N A， AL-BAHI A M， KADA B. ANN-based failure modeling of T-56 engine turbine［C］∥2023 Annual Reliability and Maintainability Symposium. 2023： 23-26. |
| 13 | LIU H， SUN J， LEI S， et al. In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data［J］. Propulsion and Power Research， 2021， 10（4）： 360-373. |
| 14 | FINN C， ABBEEL P， LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks［DB/OL］. arXiv preprint： 1703.03400， 2017. |
| 15 | ANDRYCHOWICZ M， DENIL M， GOMEZ S， et al. Learning to learn by gradient descent by gradient descent［DB/OL］. arXiv preprent： 1606.04474， 2016. |
| 16 | HOUTHOOFT R， CHEN R Y， ISOLA P， et al. Evolved policy gradients［DB/OL］. arXiv preprint： 1802.04821， 2018. |
| 17 | SUNG F， YANG Y， ZHANG L， et al. Learning to compare： Relation network for few-shot learning［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018： 18-23. |
| 18 | RUSU A， RAO D， SYGNOWSKI J， et al. Meta-learning with latent embedding optimization［DB/OL］. arXiv preprint： 1807.05960， 2019. |
| 19 | 周登极， 张会生， 苏明. 一种燃气轮机涡轮叶片蠕变损伤评估模型及其应用［J］. 动力工程学报， 2015， 35（12）： 964-969. |
| ZHOU D J， ZHANG H S， SU M. An evaluation model for the creep damage of gas turbine blades and its application［J］. Journal of Chinese Society of Power Engineering， 2015， 35（12）： 964-969 （in Chinese）. | |
| 20 | CHOI J Y， LEE B. Combining LSTM network ensemble via adaptive weighting for improved time series forecasting［J］. Mathematical Problems in Engineering， 2018， 2018： 2470171. |
| 21 | 黄炎， 葛思源， 翟慕赛， 等. 环境温度影响下基于LSTM神经网络识别结构损伤［J］. 计算力学学报， 2024， 41（2）： 248-255. |
| HUANG Y， GE S Y， ZHAI M S， et al. Structural damage identification based on LSTM neural networks under ambient temperature variations［J］. Chinese Journal of Computational Mechanics， 2024， 41（2）： 248-255 （in Chinese）. | |
| 22 | CHOE D， KIM H， KIM M. Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades［J］. Renewable Energy， 2021，174： 218-235. |
| 23 | GREFF K， SRIVASTAVA R K， KOUTNIK J， et al. LSTM： A search space odyssey［J］. IEEE Transactions on Neural Networks and Learning Systems， 2017， 28（10）： 2222-2232. |
/
| 〈 |
|
〉 |
CJA