| Citation: | MAO Haoying, SUN Youchao, LI Longbiao, et al. Aeroengine fault risk early warning model based on improved DRSN[J]. Journal of Aerospace Power, 2024, 39（2）：20210473 doi: 10.13224/j.cnki.jasp.20210473
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Aero-engine is a kind of mechanical equipment with possible multi-fault risk. The application of advanced computing training method can effectively realize accurate risk early warning analysis, and provide reference for the guidance of engine operation and maintenance. Multivariable time series samples were extracted from the early warning symptom data set of engine failure risk, and the samples were matrix-transformed into gray scale samples. Image samples were preprocessed and enhanced, and sequence sample tags were thermally encoded. Deep attention mechanism and residual shrinkage block with threshold were integrated into the deep residual shrinkage network (DRSN), so as to obtain high discriminant features and realize soft thresholding. Combining long short term memory layers with multiple hidden layers, DRSN model was improved, and principal component analysis was made to reconstruct features and extract principal components. The cumulative interpretable variance contribution rate was 93.7%. The training accuracy for identifying, classifying, and warning 20 potential fault symptoms was 96.1%. An improved early warning DRSN model of engine fault risk was proposed. Compared with other algorithms, this model of strong robustness improved the accuracy by at least 4.4%.
| [1] |
皮骏,马圣,张奇奇,等. 基于改进果蝇算法优化的GRNN航空发动机排气温度预测模型[J]. 航空动力学报,2019,34(1): 8-17. PI Jun,MA Sheng,ZHANG Qiqi,et al. Aero-engine exhaust gas temperature prediction model based on IFOA-GRNN[J]. Journal of Aerospace Power,2019,34(1): 8-17. (in Chinese
|
| [2] |
DING Shuiting,ZHOU Huimin,PAN Bochao,et al. An experimental method to obtain the hard alpha anomaly distribution for titanium alloy aeroengine disks[J]. Chinese Journal of Aeronautics,2021,34(4): 67-82. doi: 10.1016/j.cja.2020.08.024
|
| [3] |
丁水汀,张弓,蔚夺魁,等. 航空发动机适航概率风险评估方法研究综述[J]. 航空动力学报,2011,26(7): 1441-1451. DING Shuiting,ZHANG Gong,YU Duokui,et al. Review of probabilistic risk assessment on aero-engine airworthiness[J]. Journal of Aerospace Power,2011,26(7): 1441-1451. (in Chinese
|
| [4] |
WU Yuting,YUAN Mei,DONG Shaopeng,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks[J]. Neurocomputing,2018,275: 167-179. doi: 10.1016/j.neucom.2017.05.063
|
| [5] |
YANG Shuming,QIU Jing,LIU Guanjun. Hierarchical model-based approach to testability modeling and analysis for PHM of aerospace systems[J]. Journal of Aerospace Engineering,2014,27(1): 131-139. doi: 10.1061/(ASCE)AS.1943-5525.0000203
|
| [6] |
王奕惟,莫李平,王奕首,等. 基于全航段QAR数据和卷积神经网络的航空发动机状态辨识[J]. 航空动力学报,2021,36(7): 1556-1563. WANG Yiwei,MO Liping,WANG Yishou,et al. Aero-engine status identification based on full-segment QAR data and convolutional neural network[J]. Journal of Aerospace Power,2021,36(7): 1556-1563. (in Chinese doi: 10.13224/j.cnki.jasp.20200431
|
| [7] |
李娟,周东华,司小胜,等. 微小故障诊断方法综述[J]. 控制理论与应用,2012,29(12): 1517-1529. LI Juan,ZHOU Donghua,SI Xiaosheng,et al. Review of incipient fault diagnosis methods[J]. Control Theory & Applications,2012,29(12): 1517-1529. (in Chinese
|
| [8] |
肖乾浩. 基于机器学习理论的机械故障诊断方法综述[J]. 现代制造工程,2021(7): 148-161. XIAO Qianhao. Review on mechanical fault diagnosis methods based on machine learning theories[J]. Modern Manufacturing Engineering,2021(7): 148-161. (in Chinese
|
| [9] |
尹宝才,王文通,王立春. 深度学习研究综述[J]. 北京工业大学学报,2015,41(1): 48-59. YIN Baocai,WANG Wentong,WANG Lichun. Review of deep learning[J]. Journal of Beijing University of Technology,2015,41(1): 48-59. (in Chinese
|
| [10] |
马波,赵祎,齐良才. 变分自编码器在机械故障预警中的应用[J]. 计算机工程与应用,2019,55(12): 245-249,264. MA Bo,ZHAO Yi,QI Liangcai. Application of variational auto-encoder in mechanical fault early warning[J]. Computer Engineering and Applications,2019,55(12): 245-249,264. (in Chinese
|
| [11] |
LI Yingshun,DONG Wan,YI Xiaojian,et al. Engine fault prediction and health evaluation based on oil sensor[C]//2020 Prognostics and Health Management Conference. Piscataway,US: IEEE,2020: 223-228.
|
| [12] |
ZHANG Zhijin,LI He,CHEN Lei. Deep residual shrinkage networks with self-adaptive slope thresholding for fault diagnosis[C]// Proceedings of 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations. Guangzhou: IEEE,2021: 236-239.
|
| [13] |
ZHAO Minghang,ZHONG Shisheng,FU Xuyun,et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics,2020,16(7): 4681-4690. doi: 10.1109/TII.2019.2943898
|
| [14] |
LIU Jiuzhou,ZHOU Qianwen,ZHANG Biyin. Deep residual shrinkage network for few-shot learning[C]// Proceedings of 2021 3rd International Conference on Intelligent Control,Measurement and Signal Processing and Intelligent Oil Field. Xi'an: IEEE,2021: 120-124.
|
| [15] |
CHE Changchang,WANG Huawei,NI Xiaoping,et al. Fault diagnosis of rolling bearing based on deep residual shrinkage network[J]. Journal of Beijing University of Aeronautics and Astronautics,2021,47(7): 1399-1406.
|
| [16] |
VIANNA M R,IZQUIERDO L A,BARROS D M,et al. Short- and long-term memory: differential involvement of neurotransmitter systems and signal transduction cascades[J]. Anais Da Academia Brasileira De Ciencias,2000,72(3): 353-364. doi: 10.1590/S0001-37652000000300009
|
| [17] |
ZOU Hui,XUE Lingzhou. A selective overview of sparse principal component analysis[J]. Proceedings of the IEEE,2018,106(8): 1311-1320. doi: 10.1109/JPROC.2018.2846588
|
| [18] |
HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,US: IEEE,2016: 770-778.
|
| [19] |
刘元芳. 航空发动机气路故障的智能诊断方法研究[D]. 厦门: 厦门大学,2018. LIU Yuanfang. Research on intelligent diagnosis method for aircraft engine gas path fault[D]. Xiamen: Xiamen University,2018. (in Chinese
LIU Yuanfang. Research on intelligent diagnosis method for aircraft engine gas path fault[D]. Xiamen: Xiamen University, 2018. (in Chinese)
|
| [20] |
郝英. 基于智能技术的民航发动机故障诊断和寿命预测研究[D]. 南京: 南京航空航天大学,2006. HAO Ying. Research on civil aviation engine fault diagnosis and life predication based on intelligent technologies[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2006. (in Chinese
HAO Ying. Research on civil aviation engine fault diagnosis and life predication based on intelligent technologies[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006. (in Chinese)
|
| [21] |
瞿红春. 民用涡扇发动机健康智能监控技术的研究[D]. 天津: 天津大学,2010. QU Hongchun. Study on civil turbofan engine health intelligent monitoring technologies[D]. Tianjin: Tianjin University,2010. (in Chinese
QU Hongchun. Study on civil turbofan engine health intelligent monitoring technologies[D]. Tianjin: Tianjin University, 2010. (in Chinese)
|
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