Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Xuyun FU; Hui LUO; Shisheng ZHONG; Lin LIN

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Aircraft engine fault detection based on grouped convolutional denoising autoencoders

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摘要

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System(ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.

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《中国航空学报：英文版》 |2019年第2期|296-307|共12页
作者
Xuyun FU; Hui LUO; Shisheng ZHONG; Lin LIN;
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作者单位

Department of Mechanical Engineering;

Harbin Institute of Technology at Weihai;

School of Mechatronics Engineering;

Harbin Institute of Technology;

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原文格式 PDF
正文语种 chi
中图分类故障分析及排除;
关键词
Aircraft; engines; Anomaly; detection; Convolutional; Neural; Network(CNN); Denoising; autoencoder; Engine; health; management; Fault; detection;

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

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