The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

Piezoelectric sensors and actuators are the bridge between electronic and mechanical systems in structures. This type of sensor is a key element in the integrity monitoring of aeronautic structures, bridges, pressure vessels, wind turbine blades, and gas pipelines. In this paper, an all-in-one system for Structural Health Monitoring (SHM) based on ultrasonic waves is presented, called Phased Array Monitoring for Enhanced Life Assessment. This integrated instrument is able to generate excitation signals that are sent through piezoelectric actuators, acquire the received signals in the piezoelectric sensors, and carry out signal processing to check the health of structures. To accomplish this task, the instrument uses a piezoelectric phased-array transducer that performs the actuation and sensing of the signals. The flexibility and strength of the instrument allow the user to develop and implement a substantial part of the SHM technique using Lamb waves. The entire system is controlled using configuration software and has been validated through functional, electrical loading, mechanical loading, and thermal loading resistance tests.

Structural Health Monitoring (SHM) of aircraft structures, which are manufactured more and more using composite material, is a well-established technique for increasing reliability and reducing maintenance costs. Implementing damage inspection algorithms that run in dedicated electronic systems helps reduce big data processing time with improved results. RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm, developed some years ago, provides a technique to identify and characterize damages both in metallic and composite materials. However, this algorithm sometimes provides inaccurate predictions of the damage location, caused mainly by the influence of path intersection points among transducers, that can mask the current location of damages. This work presents a geometrical modification of standard RAPID algorithm (RAPID-G) that mitigates the influence of the intersection point between sensor paths. The results highlight that the location of damages improves significantly with this modification of the algorithm.

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure’s surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

This paper describes an architecture model for data acquisition systems based on compact PCI platforms. The aim is to increase real-time data processing capabilities in experimental environments such as nuclear fusion devices (e.g., ITER). The model has these features: 1) Real-time data acquisition: the system has been provided with real-time capabilities, developing specific data acquisition kernel modules under Linux and RTAI, using COMEDI project drivers; 2) Multiprocessor PCI eXtensions for Instrumentation (PXI) Architecture: the model makes possible to add one or more processing cards (in nonsystem slots) to each standard PXI chassis. Several real-time software modules have been developed to allow the communication between the PXI controller and the processing cards. This way the system performance is not restricted to the PXI controller's own performance. This model provides scalability to the system, adding or removing processing cards; 3) Real-time acquired data distribution: with this model it is possible to define how to distribute, in real-time, the data from all acquired signals in the system among the processing cards and the PXI controller; and 4) Dynamic Data Processing: a software platform has been developed to allow users to dynamically manage their own data processing algorithms in the system. This means that users can start, stop, modify, and replace their data processing algorithms without disrupting the data acquisition process or the rest of the data processing algorithms.