The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures.

Carbon fiber and glass fiber-reinforced polymer composites are extensively used in many industries such as aerospace, wind energy, and automobile. Nowadays, due to the high interest in manufacturing safe and cost-optimized composite structures, the need for nondestructive-evaluation (NDE) of these structures is rising significantly; either as a part of the manufacturing process or while the structure is in service. Among all NDE techniques, one of the most promising for composite materials is ultrasonic testing (UT). Ultrasonic NDE uses low amplitude acoustic/elastic waves with frequencies higher than about 20kHz to evaluate components without affecting the material properties and their integrity. This technique is extensively employed for the inspection of parts during in-service use, for quality control during the manufacturing process, as well as for material characterizations. The main advantages of the ultrasonic NDE technique are the possibility of high penetration in many commonly used materials, robustness against the conductivity of the material, accuracy in locating and measuring defects, and the ability to be performed with one-sided access. However, there are some drawbacks such as the need for a highly experienced operator and the necessity of maintaining a high degree of coupling between the transducer and the surface of the structure. While conventional ultrasonic NDE using single-element transducers has been commonly used in the past, it has some limitations for the evaluation of thick composite layers, such as the high attenuation and low signal-to-noise ratio. The phased array ultrasonic technique (PAUT) significantly improves the capability of UT for the inspection of composite materials as the ultrasonic waves can be generated and focused at desired energies, angles, and distances. This chapter is organized to provide the essential theoretical background and technical knowledge on ultrasonic NDE of thick composite laminates, starting from the physics of ultrasonics and phased array systems, going up to the signal-processing, considerations on how to deal with the collected data, and finally, the real-world applications.

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

In this paper, to increase the performance of the sparse reconstruction method in real complex engineering structures an adaptive dictionary learning framework is proposed which updates the dictionary matrix, to allow improved compatibility with the complex structure. This proposed framework was developed by combining analytical modeling with training data sets and learning methods. An experimental evaluation of the proposed dictionary learning framework was performed on an anisotropic composite plate with a stiffener. In this experimental evaluation, a moving magnet was used as the artificial damage to capture the training data set, and both artificial damage in several locations and real impact damage was used for detection and location of the target damage. The obtained results confirmed the concept of the proposed dictionary learning framework for the improved health monitoring of complex structures.

To perform active structural health monitoring (SHM), guided waves (GW) have received great interest as they can inspect large areas with a few sensors and are sensitive to barely-visible structural damages. Fiber Bragg grating (FBG) sensors offer several advantages such as small size, low weight and ability to be embedded but their use has been limited for GW sensing due to their limited sensitivity while using spectrometers. FBG sensors in the edge-filtering configuration have overcome this issue with reasonable sensitivity and there is a renewed interest in their use. It is well known that when subjected to a transverse strain, the circular cross-section of the fiber deforms into an elliptical shape generating the birefringence phenomenon. This deformation, influences the coupling mode of the light inside the FBG and hence, modifies the resulting reflectivity spectrum. This paper investigates how controlled changes in the reflectivity spectrum can be introduced using different transverse loads. The effect of the modified spectrum on the sensitivity of the FBG for GW measurements is then studied. The study also investigates the effect of the transverse strain on the coupling of the GW from the structure into the fiber.

In this paper, the performance of the sparse reconstruction (SR) and the delay-and-sun (DAS) methods for damage localization, were evaluated for various environmental and operational conditions, both numerically and experimentally. To assess these damage localization methods, a methodology based on the Taguchi method was used to make the experimental design, and a modified performance-index was defined to represent the quality of reconstructed images. Then, the robustness and the accuracy of each method, in a well-defined performance region relevant to in-service aerospace structures, were investigated using the Taguchi and analysis of variance methods. It was concluded that for the defined conditions, the robustness of the delay and sum method is better than the sparse reconstruction method for uncontrolled factors. However, the sparse reconstruction method is more robust to poor baseline subtraction than the delay and sum method, while the delay and sum method was more robust to factors that lead to a model mismatch. These results provide additional insight into the design of reliable accurate structural health monitoring systems. The outcomes of this work can be used in future reaserch into SHM imaging techniques.

To perform active structural health monitoring, guided Lamb waves for damage detection have recently gained extensive attention. Many algorithms are used for damage detection with guided waves and among them, the delay-and-sum method is the most commonly used algorithm because of its robustness and simplicity. However, delay-and-sum images tend to have poor accuracy with a large spot size and a high noise floor, especially in the presence of multiple damages. To overcome these problems, another method that is based on sparse reconstruction can be used. Although the images produced by the sparse reconstruction method are superior to the conventional delay-and-sum method, it has the challenges of the time and cost of computations in comparison with the delay-and-sum method. Also, in some cases in multi-damage detection, the sparse reconstruction method totally fails. In this article, using prior support information of the structure achieved by the delay-and-sum method, a hybrid method based on sparse reconstruction method is proposed to improve the computational performance and robustness of sparse reconstruction method in the case of multi-damage presence. The effectiveness of the proposed method in detecting damages is demonstrated experimentally and numerically on a simple aluminum plate. The technique is also shown to accurately identify and localize multi-site damages as well as single damage with low sampled signals.