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In order to solve the problems that traditional aero-engine anomaly detection methods are difficult to deal with unbalanced samples, low accuracy and generalization performance, and insufficient data distribution consideration, a deep support vector data description based on mixed kernel maximum correntropy criterion ( MKMCC-DSVDD) was proposed. Firstly, the synthetic minority oversampling technique (SMOTE) was used to expand the abnormal sample size and improve the generalization performance of nonequilibrium sample. Then, the maximum correlation entropy loss function based on hybrid kernel improvement was established and analyzed to improve the accuracy without data distribution assumption. Finally, an aero engine anomaly detection method based on MKMCC-DSVDD was constructed. The abnormal state average area under curve ( AUC) reaches 98. 53% in the test of anomaly detection of a certain aero-engine gas path system and oil system, which indicates that MKMCC-DSVDD anomaly detection method has high applicability and generalization performance. [ABSTRACT FROM AUTHOR]

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Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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