Detection of impact on aircraft composite structure using machine learning techniques

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.

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

“…Stone and Krishnamurthy 144 proposed a thrust force controller using an ANN to reduce the development of delaminations caused by the entry and removal of a drill bit to an FRP. Machine learning was employed to classify the Visual inspection [85][86][87][88][89][90] Ultrasonic inspection [91][92][93][94] Eddy current [95][96][97][98][99] Radiography 100-105 (e.g., x-ray) Thermography 97,[106][107][108][109][110] Acoustic emission [111][112][113][114][115] Fiber optic sensors [116][117][118][119][120][121] (e.g., fiber Bragg grating) Piezoelectric transducers [122][123][124][125] Laser vibrometry [126][127][128][129][130][131][132] failure methods of composite plates bolted together. 145 A beneficial application of ML is prediction making.…”

Applications of Machine Learning in Aircraft Maintenance

“…Results showed that the hybrid data preparation model is effective at forecasting the equipment failure rate. Ai et al [34] showed how machine learning algorithms can be implemented to detect impacts on aircraft composite structures which describes a promising technique for automatically detecting and localizing a debris or hail impact during flight. Acoustic emission (AE) was used as an impact surveillance method to attain this purpose.…”

“…One main objective of this method is to be able to accurately forecast when it is time to repair or replace a component [29]. The advantages of prolonged usage are lost if done too far in advance; if done too late, unforeseen failures can occur, lowering asset availability [34]. As a result, improving component lifetime estimates accuracy is a continuous priority [37].…”

Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques

“…Recent advances in machine learning and artificial intelligence have paved the way for researchers in academia and industry to predict the specific material behaviors from sensor data and structural health monitoring [ 27 , 28 , 29 , 30 , 31 ]. Liu et al incorporated the acoustic emission technique and K-means clustering method to identify damage modes in wind turbine blade composites [ 32 , 33 ].…”