This chapter provides an overview of the use of strain sensors for structural health monitoring. Compared to acceleration-based sensors, strain sensors can measure the deformation of a structure at very low frequencies (up to DC) and enable the measurement of ultrasonic responses. Many existing SHM methods make use of strain measurement data. Furthermore, strain sensors can be easily integrated in (aircraft) structures. This chapter discusses the working principle of traditional strain gauges (Sect. 8.1) and different types of optical fiber sensors (Sect. 8.2). The installation requirements of strain sensors and the required hardware for reading out sensors are provided. We will also give an overview of the advantages and the limitations of commonly used strain sensors. Finally, we will present an overview of the applications of strain sensors for structural health monitoring in the aeronautics field.

This study presents the results of acoustic emission (AE) measurements and characterization in the loading of biocomposites at room and low temperatures that can be observed in the aviation industry. The fiber optic sensors (FOS) that can outperform electrical sensors in challenging operational environments were used. Standard features were extracted from AE measurements, and a convolutional autoencoder (CAE) was applied to extract deep features from AE signals. Different machine learning methods including discriminant analysis (DA), neural networks (NN), and extreme learning machines (ELM) were used for the construction of classifiers. The analysis is focused on the classification of extracted AE features to classify the source material, to evaluate the predictive importance of extracted features, and to evaluate the ability of used FOS for the evaluation of material behavior under challenging low-temperature environments. The results show the robustness of different CAE configurations for deep feature extraction. The combination of classic and deep features always significantly improves classification accuracy. The best classification accuracy (80.9%) was achieved with a neural network model and generally, more complex nonlinear models (NN, ELM) outperform simple models (DA). In all the considered models, the selected combined features always contain both classic and deep features.

Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for biocomposite and glass fiber epoxy (GFE) composites at room temperature and low temperature. For the acquisition of AE signals, fiber optic sensors (FOS) are used that can outperform classical electrical sensors under challenging operational environments. In this paper, we propose the extraction of deep features using different machine learning methods. The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through discriminant analysis, neural networks, and extreme learning machines, combined with feature selection, to estimate the predictive potential of various feature sets. The proposed signal processing structure is focused on the classification of AE signals to recognize the source material, evaluate the predictive importance of extracted features, and evaluate the ability of used FOS for evaluation of material behavior under challenging low-temperature environments.