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. Index Terms-Structural health monitoring, composite, RAPID, Lamb waves, guided waves, damage detection, damage characterization. I. INTRODUCTIONS TRUCTURAL Health Monitoring (SHM) systems integrate current non-destructive testing (NDT) techniques in structures, facilitating their status analysis and monitoring [1]. This implementation reduces maintenance costs and increases aeronautical structures' safety and reliability. This technology aims to achieve the highest level of automation for monitoring and providing data from structures in real-time. Lamb waves ultrasound testing [2] is one of the most widely used techniques for damage detection in aircraft structures. These waves (mechanical elastic) propagate through small thick material,

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PurposeThe purpose of this paper is to develop an efficient tool to ensure traceability data entry based on RFID and improve the detection of fakes inside the supply chain.Design/methodology/approachFirst, the authors put forward a secure traceability system with a cryptographic operator that improves efficiency, saves costs and is adaptable to all types of supply chains. Second, the authors implement the system in a food manufacturing company, show the compatibility of the system with the initial production chain and demonstrate its improvement.FindingsThe authors have economically and technologically improved the detection of faked goods in the supply chain by means of a cheap and scalable system based on RFID.Research limitations/implicationsThe system has been tested in a manufacturing point of the supply chain, not in the supply chain as a whole. As traceability is additive in character, the results can be generalized and applied to each part of the supply chain.Practical implicationsA prototype has been built and implemented for a food manufacturing company. It meets all the specifications required for detection of fakes in manufactured products.Originality/valueUnlike other existing products used to detect fakes through traceability, this system identifies fakes and potential counterfeits, significantly reducing the cost of security management. Another important difference is that verification can be performed by a third party not involved in the system, without loss of security.

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.

Structural Health Monitoring (SHM) of Carbon Fiber Reinforced Polymers (CFRP) has become, recently, in a promising methodology for the field of Non-Destructive Inspection (NDI), specially based on Ultrasonic Guided Waves (UGW), particularly Lamb waves using Piezoelectric Transducers (PZT). However, the Environmental and Operational Conditions (EOC) perform an important role on the physical characteristics of the waves, mainly the temperature. Some of these effects are phase shifting, amplitude changes and time of flight (ToF) variations. In this paper, a compensation method for evaluating and compensating the effects of the temperature is carried out, performing a data-driven methodology to calculate the features from a dataset of typical temperature values obtained from a thermoset matrix pristine plate, with a transducer network attached. In addition, the methodology is tested on the same sample after an impact damage is carried out on it, using RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) and its geometrical variant (RAPID-G) to calculate the location of the damage.