A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system

Zhen Jia, Yang Li, Shengdong Wang, Zhenbao Liu

科研成果: 期刊稿件文章同行评审

15 引用 (Scopus)

摘要

The effectiveness and safety of an aircraft’s flight depend heavily on the flight control system. Since the attitude sensor is the weakest link, identifying its failure modes is crucial. To overcome the shortcomings of a single diagnosis model and a single input signal, this paper proposes a hybrid deep fault diagnosis model based on multi-data fusion. First, the normal and fault models of the sensor are established, and the residual timing signals of the sensor in different fault states are obtained. The frequency domain and timefrequency domain representations of the original timing signals are collected by means of fast Fourier transform and S-transform, and they are used as the input of the hybrid deep diagnosis model. The deep model is designed for the three inputs to mine the characteristics of the input data. These three deep features are concatenated and dimensionally reduced to obtain more comprehensive and representative features. Finally, the classifier is used to classify and obtain the diagnosis results. Through experiments, the advantages of the proposed method are verified by comparing it with several other methods.

源语言英语
文章编号025115
期刊Measurement Science and Technology
34
2
DOI
出版状态已出版 - 2月 2023

指纹

探究 'A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system' 的科研主题。它们共同构成独一无二的指纹。

引用此