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

Cui, Langfu and Zhang, Chaoqi and Zhang, Qingzhen and Wang, Junle and Wang, Yixuan and Shi, Yan and Lin, Cong and Jin, Yang (2021) A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM. Aerospace, 8 (12). p. 374. ISSN 2226-4310

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Abstract

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.

Item Type: Article
Subjects: Apsci Archives > Engineering
Depositing User: Unnamed user with email support@apsciarchives.com
Date Deposited: 29 Mar 2023 05:41
Last Modified: 11 May 2024 08:50
URI: http://eprints.go2submission.com/id/eprint/546

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