A Deep Neural Network-Based Fault Detection Scheme for Aircraft IMU Sensors
A new fault detection scheme for aircraft Inertial Measurement Unit (IMU) sensors is developed in this paper. This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). The fault detection network (FDN) developed in t...
Saved in:
Main Authors: | Yiming Zhang, Hang Zhao, Jinyi Ma, Yunmei Zhao, Yiqun Dong, Jianliang Ai |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | International Journal of Aerospace Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/3936826 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors
by: Yunmei Zhao, et al.
Published: (2022-01-01) -
Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks
by: Liming Zhou, et al.
Published: (2021-01-01) -
Design and Analysis of Robust Fault Diagnosis Schemes for a Simulated Aircraft Model
by: M. Benini, et al.
Published: (2008-01-01) -
IMU Sensor-Based Worker Behavior Recognition and Construction of a Cyber–Physical System Environment
by: Sehwan Park, et al.
Published: (2025-01-01) -
Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure
by: Sanghyuk Lee, et al.
Published: (2014-01-01)