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Open AccessArticle

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

1
Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China
2
Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
3
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
4
Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1379; https://doi.org/10.3390/s18051379
Received: 31 March 2018 / Revised: 24 April 2018 / Accepted: 24 April 2018 / Published: 29 April 2018
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. View Full-Text
Keywords: self-sensing wing; feature extraction; feature selection; flight state identification; machine learning self-sensing wing; feature extraction; feature selection; flight state identification; machine learning
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MDPI and ACS Style

Chen, X.; Kopsaftopoulos, F.; Wu, Q.; Ren, H.; Chang, F.-K. Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches. Sensors 2018, 18, 1379.

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