A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM

被引:10
|
作者
Cui, Langfu [1 ]
Zhang, Chaoqi [1 ]
Zhang, Qingzhen [1 ]
Wang, Junle [1 ]
Wang, Yixuan [1 ]
Shi, Yan [1 ]
Lin, Cong [2 ]
Jin, Yang [1 ]
机构
[1] Beijing Univ Aeronaut & Astronaut, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[2] Sichuan Aviat Maintenance Engn Dept, Chengdu 610207, Peoples R China
关键词
aero-engine gas path; anomaly detection; Markov Transition Field; hierarchical clustering; LSTM; Gaussian mixture model;
D O I
10.3390/aerospace8120374
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy of aero-engine gas path anomaly detection, a method based on Markov Transition Field and LSTM is proposed in this paper. The correlation among high-dimensional QAR data is obtained based on Markov Transition Field and hierarchical clustering. According to the correlation analysis of high-dimensional QAR data, a multi-input and multi-output LSTM network is constructed to realize one-step rolling prediction. A Gaussian mixture model of the residuals between predicted value and true value is constructed. The three-sigma rule is applied to detect outliers based on the Gaussian mixture model of the residuals. The experimental results show that the proposed method has high accuracy for aero-engine gas path anomaly detection.
引用
收藏
页数:15
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