A novel method for aeroengine performance model reconstruction based on CDAE model |
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| Affiliation: | 1. PHM Laboratory, Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel;2. Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States;1. Sino-French Engineering School, Beihang University, China;2. School of Mechanical Engineering and Automation, Beihang University, China;3. Beige Institute, China;4. Jingdezhen Research Institute of Beihang University, China;5. Ningbo Research Institute of Beihang University, China;1. School of Rail Transportation, Soochow University, Suzhou, Jiangsu 215000, China;2. Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore;1. College of Mechanical Engineering, Donghua University, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;3. J. Mike Walker Department of Mechanical Engineering, The University of Texas at Austin, USA;4. Department of Mechanical and Aerospace Engineering, University of Central Florida, USA;5. Department of Mechanical Engineering at the School of Engineering, Cardiff University, UK;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Abstract: | Aeroengine is a complex multi-module system. Due to the limitation of sensor cost and sensor installation conditions, it is usually impossible to install a large number of sensors to measure the physical parameters of the aeroengine modules to establish the accurate module characteristic models to achieve the purpose of module performance evaluation. To address this issue, the high-dimensional physical field reconstruction strategy base on limited measurement data is developed, which is of great significance to the modeling of module characteristics. A reconstruction framework of a high-dimensional physical field based on limited measurement data is built. The mapping relationship between limited measurement data and high-dimensional physical field data is established, and the relevant learning strategies based on the deep learning network are designed. To verify the effectiveness of the proposed method, the simulation dataset generated by the multi-component closed-loop simulation system and the aeroengine service dataset are used for experimental verification, and the mean and variance of mean square error are used as evaluation indexes. Experimental results show that the proposed method can obtain high-dimensional physical field distribution based on limited measurement data. |
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| Keywords: | Aeroengine Characteristic space Unsupervised learning Autoencoder Mapping model |
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