A hybrid deep computation model for feature learning on aero-engine data: applications to fault detection |
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| Institution: | 1. Key Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi''an, China;2. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706–1572, United States;3. Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University, Xi''an, China;1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China;2. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China;3. School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology,Hangzhou, China;1. School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China;2. Department of Public Basic Courses, Nanjing Institute of Industry Technology, Nanjing, Jiangsu 210023, China |
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| Abstract: | Recently, the safety of aircraft has attracted much attention with some crashes occurring. Gas-path faults, as the most common faults of aircraft, pose a vast challenge for the safety of aircraft because of the complexity of the aero-engine structure. In this article, a hybrid deep computation model is proposed to effectively detect gas-path faults on the basis of the performance data. In detail, to capture the local spatial features of the gas-path performance data, an unfully connected convolutional neural network of one-dimensional kernels is used. Furthermore, to model the temporal patterns hidden in the gas-path faults, a recurrent computation architecture is introduced. Finally, extensive experiments are conducted on real aero-engine data. The results show that the proposed model can outperform the models with which it is compared. |
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