Next Article in Journal
Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network
Next Article in Special Issue
A Novel Surface Descriptor for Automated 3-D Object Recognition and Localization
Previous Article in Journal
Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture
Previous Article in Special Issue
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1—PZT Diaphragm Transducer Response and EMI Sensing Technique
Open AccessArticle

A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification

1
School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China
3
Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
4
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
5
Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(2), 275; https://doi.org/10.3390/s19020275
Received: 26 November 2018 / Revised: 27 December 2018 / Accepted: 2 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)
The vibration of a wing structure in the air reflects coupled aerodynamic–mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles. View Full-Text
Keywords: self-sensing wing; dual-tree complex-wavelet packet transformation; convolution neural network; grey-wolf optimizer; flight-state identification self-sensing wing; dual-tree complex-wavelet packet transformation; convolution neural network; grey-wolf optimizer; flight-state identification
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.3390/s18051379
    Link: https://www.mdpi.com/1424-8220/18/5/1379
    Description: This submitted manuscript shares the same background and experiment data with the previous published one. The difference is the methodology development, results and discussion.
MDPI and ACS Style

Chen, X.; Kopsaftopoulos, F.; Wu, Q.; Ren, H.; Chang, F.-K. A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification. Sensors 2019, 19, 275.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop