| Citation: | WANG Xuemin, XU Jingpei, HE Yun. Stress and temperature prediction of aero-engine compressor disk based on multilayer perceptron[J]. Journal of Aerospace Power, 2024, 39（4）：20220297 doi: 10.13224/j.cnki.jasp.20220297
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Taking the measures parameters of the engine as the initial characteristics, the MLP （multilayer perceptron） model of aero-engine compressor disk stress and temperature prediction was established by using artificial neural network technology, and BP （back propagation） neural network algorithm was used for training. The results showed that the prediction results of this method were in good agreement with the traditional finite element calculation results. The relative deviations were all within 1%, the determination coefficients were above 0.95, and the root mean squared error was within 5. Moreover, the calculation speed increased from hour level to minute second level, providing a basis for subsequent engineering applications.
| [1] |
钱文学. 某型航空发动机低压压气机轮盘疲劳可靠性分析[D]. 沈阳: 东北大学,2006. QIAN Wenxue. Fatigue reliability analysis of aeroengine low pressure compressor disk[D]. Shenyang: Northeastern University,2006. (in Chinese
QIAN Wenxue. Fatigue reliability analysis of aeroengine low pressure compressor disk[D]. Shenyang: Northeastern University, 2006. (in Chinese)
|
| [2] |
张成龙. 某型航空发动机低压压气机轮盘疲劳寿命评估与损伤分析[D]. 沈阳: 东北大学,2017. ZHANG Chenglong. Fatigue life evaluation and damage analysis of aeroengine low pressure compressor disk[D]. Shenyang: Northeastern University,2017. (in Chinese
ZHANG Chenglong. Fatigue life evaluation and damage analysis of aeroengine low pressure compressor disk[D]. Shenyang: Northeastern University, 2017. (in Chinese)
|
| [3] |
JAW L C,MATTINGLY J D. Aircraft engine controls: design,system analysis,and health monitoring[M]. Reston,US: American Institute of Aeronautics and Astronautics,2009.
|
| [4] |
王睿乾,潘林,李其橙,等. 基于机器学习的航空发动机导管CNC弯曲回弹预测及补偿[J]. 塑性工程学报,2021,28(7): 104-109. WANG Ruiqian,PAN Lin,LI Qicheng,et al. Prediction and compensation of springback for aero-engine pipes during CNC bending based on machine learning[J]. Journal of Plasticity Engineering,2021,28(7): 104-109. (in Chinese
|
| [5] |
钟诗胜,雷达,丁刚. 卷积和离散过程神经网络及其在航空发动机排气温度预测中的应用[J]. 航空学报,2012,33(3): 438-445. ZHONG Shisheng,LEI Da,DING Gang. Convolution sum discrete process neural network and its application in aeroengine exhausted gas temperature prediction[J]. Acta Aeronautica et Astronautica Sinica,2012,33(3): 438-445. (in Chinese
|
| [6] |
刘伟民,胡忠志. 一种基于神经网络的航空发动机剩余寿命预估方法[J]. 航空发动机,2021,47(3): 8-15. LIU Weimin,HU Zhongzhi. An aeroengine remaining useful life prediction method based on neural network[J]. Aeroengine,2021,47(3): 8-15. (in Chinese
|
| [7] |
LOFTIS C,YUAN Kunpeng,ZHAO Yong,et al. Lattice thermal conductivity prediction using symbolic regression and machine learning[J]. The Journal of Physical Chemistry A,2021,125(1): 435-450. doi: 10.1021/acs.jpca.0c08103
|
| [8] |
马忠,郭建胜,顾涛勇,等. 基于改进卷积神经网络的航空发动机剩余寿命预测[J]. 空军工程大学学报(自然科学版),2020,21(6): 19-25. MA Zhong,GUO Jiansheng,GU Taoyong,et al. A remaining useful life prediction for aero-engine based on improved convolution neural networks[J]. Journal of Air Force Engineering University (Natural Science Edition),2020,21(6): 19-25. (in Chinese
|
| [9] |
张弛,付相君,周先颖,等. 基于MLP的相关路段流量预测模型[J]. 重庆理工大学学报(自然科学),2021,35(8): 129-135. ZHANG Chi,FU Xiangjun,ZHOU Xianying,et al. Traffic flow forecasting model of correlated roads based on MLP[J]. Journal of Chongqing University of Technology (Natural Science),2021,35(8): 129-135. (in Chinese
|
| [10] |
吕永生,钱吉生,韩建平,等. 应用人工神经网络技术建立航空配餐微生物生长模型[J]. 食品科技,2010,35(4): 104-107,113. LÜ Yongsheng,QIAN Jisheng,HAN Jianping,et al. Development of microbiological growth model for flight catering with the technology of artificial neural networks[J]. Food Science and Technology,2010,35(4): 104-107,113. (in Chinese
|
| [11] |
丁雪松,黄立群,张步忠,等. 基于多层感知机的蛋白质变性温度预测[J]. 计算机应用研究,2019,36(8): 2421-2423. DING Xuesong,HUANG Liqun,ZHANG Buzhong,et al. Using multi-layer perceptron to predict protein melting temperature[J]. Application Research of Computers,2019,36(8): 2421-2423. (in Chinese
|
| [12] |
KRUSE R,BORGELT C,KLAWONN F,et al. Multi-layer perceptrons[M]//Computational Intelligence. London,UK: Springer,2013: 47-81.
|
| [13] |
刘苗苗,蒋艳. 基于改进多层感知机模型的港口吞吐量预测研究[J]. 软件工程,2021,24(3): 39-42,35. LIU Miaomiao,JIANG Yan. Research on port throughput forecast based on improved multilayer perceptron model[J]. Software Engineering,2021,24(3): 39-42,35. (in Chinese
|
| [14] |
DUMPALA S H,CHAKRABORTY R,KOPPARAPU S K. A novel data representation for effective learning in class imbalanced scenarios[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. New York,US: ACM,2018: 2100-2106.
|
| [15] |
张有健,陈晨,王再见. 深度学习算法的激活函数研究[J]. 无线电通信技术,2021,47(1): 115-120. ZHANG Youjian,CHEN Chen,WANG Zaijian. Research on activation function of deep learnimg algorithm[J]. Radio Communications Technology,2021,47(1): 115-120. (in Chinese
|
| [16] |
GLOROT X,BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[R]. Sardinia,Italy: International Conference on Artificial Intelligence and Statistics,2010.
|
| [17] |
杨兴宇,朱锐锋,郑小梅,等. 航空燃气涡轮发动机寿命消耗监测技术及应用[C]//航空装备维修技术及应用研讨会论文集. 烟台,2015: 843-849.
|
| [18] |
程都. 基于神经网络的航空发动机模型自适应修正[D]. 辽宁,大连: 大连理工大学,2019. CHENG Du. Adaptive correction of aeroengine model based on neural network[D]. Dalian,Liaoning: Dalian University of Technology,2019. (in Chinese
CHENG Du. Adaptive correction of aeroengine model based on neural network[D]. Dalian, Liaoning: Dalian University of Technology, 2019. (in Chinese)
|
| [19] |
SINGH D,SINGH B. Investigating the impact of data normalization on classification performance[J]. Applied Soft Computing,2020,97: 105524. doi: 10.1016/j.asoc.2019.105524
|
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