Detection of impact on aircraft composite structure using machine learning techniques
- 31 July 2021
- journal article
- research article
- Published by IOP Publishing in Measurement Science and Technology
- Vol. 32 (8), 084013
- https://doi.org/10.1088/1361-6501/abe790
Abstract
Aircraft structures are exposed to impact damage caused by debris and hail during their service life. One of the design concerns in composite structures is the resistance of layered surfaces to damage, which occurs from impacts with various foreign objects. Therefore, the impact localization and damage quantification of impacts should be studied and considered to address flight safety and to reduce costs associated with a regularly scheduled visual inspection. Since the structural components of the aircraft are large scale, visual inspection and monitoring are challenging and subject to human error. This paper presents a promising solution that can automatically detect and localize an impact that may occur during flight. To achieve this goal, acoustic emission (AE) is employed as an impact monitoring approach. Random forest and deep learning were adopted for training the source location models. An AE dataset was collected by conducting an impact experiment on a full-size thermoplastic aircraft elevator in a laboratory environment. A dataset consisting of AE parametric features and a dataset consisting of AE waveforms were assigned to a random forest classifier and deep learning network for the investigation of their applicability of impact source localization. The results obtained were compared using the source localization approach in previous research using a conventional artificial neural network. The analysis of results shows the random forest and deep learning leads to better event localization performance. In addition, the random forest model can provide the importance of features. By deleting the least important features, the storage required to save the input and the computing time for the random forest is greatly reduced, and an acceptable localization performance can still be obtained.Keywords
Funding Information
- GKN - Fokker Aerostructures BV
- National Aeronautics and Space Administration (80NSSC20M0165)
This publication has 27 references indexed in Scilit:
- Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signalsMechanical Systems and Signal Processing, 2016
- Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modelingJournal of Hydroinformatics, 2013
- Variable selection using random forestsPattern Recognition Letters, 2010
- Mass flow rate measurement in abrasive jets using acoustic emissionMeasurement Science and Technology, 2009
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- Random ForestsMachine Learning, 2001
- Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagationDecision Support Systems, 1998
- The Whittaker-Shannon sampling theorem for experimental reconstruction of free-space wave packetsJournal of Modern Optics, 1997
- Simultaneous measurement of acoustic emission and electrical resistance variation in stress-corrosion crackingMeasurement Science and Technology, 1995
- Learning long-term dependencies with gradient descent is difficultIEEE Transactions on Neural Networks, 1994