| Citation: | CHE Changchang, WANG Huawei, NI Xiaomei, et al. Fault fusion diagnosis of aero-engine based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 621-628. doi: 10.13700/j.bh.1001-5965.2017.0197(in Chinese)
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Through the fault diagnosis of aero-engine, the working status of each component can be correctly judged, and the maintenance program can be determined quickly to ensure the safety of flight. Based on the combination of deep belief network and decision fusion theory, the fault fusion diagnosis model of aero-engine based on deep learning was proposed. This model, through analyzing a large number of engine performance parameters, starts with getting fault classification confidence via hidden features in engine performance parameters extracted by deep learning algorithm, and then the multiple fault classification results were fused by decision fusion method to get more accurate results. The JT9D engine failure coefficient was simulated as data to prove the validity of the method. The results of an example show that the reliability of the data has been improved by fault fusion diagnosis of several experimental results, and the model has high fault classification and diagnosis accuracy and anti-interference ability.
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