Damage localization in composite plates using canonical polyadic decomposition of Lamb wave difference signals tensor

Monitoring in real-time and autonomously the health state of aeronautic structures is referred to as Structural Health Monitoring (SHM) and is a process decomposed in four steps: damage detection, localization, classification, and quantification. The structure under study is here a complex aeronautic nacelle and the focus is put on the localization step of the SHM process. The fact that SHM data are naturally three-way tensors is here investigated for this purpose. It is demonstrated that under classical assumptions regarding wave propagation, the canonical polyadic decomposition of rank 2 of the tensor built from the phase of the difference signals between a healthy and damaged states provides direct access to the distances between the piezoelectric elements and the damage. This property is used here to propose an original and robust tensor-based damage localization algorithm. This algorithm is successfully validated on experimental data coming from the aeronautic nacelle equipped with 30 mounted piezoelectric elements

Damage localization in composite plates using canonical polyadic decomposition of Lamb wave difference signals tensor

Monitoring in real-time and autonomously the health state of aeronautic structures is referred to as Structural Health Monitoring (SHM) and is a process decomposed in four steps: damage detection, localization, classification, and quantification. Structures under study are here composite structures representative of aeronautic applications and the focus is put on the localization step of the SHM process. The fact that SHM data are naturally three-way tensors is here investigated for this purpose. It is demonstrated that under classical assumptions regarding wave propagation, the canonical polyadic decomposition of rank 2 of the tensor built from the phase of the difference signals between a healthy and damaged states provides direct access to the distances between the piezoelectric elements and the damage. This property is used here to propose an original and robust tensor-based damage localization algorithm. This algorithm is successfully validated on experimental data coming from composite plates with mounted piezoelectric elements and compared with a classical localization algorithm based on triangulation

Damage localization in geometrically complex aeronautic structures using canonical polyadic decomposition of Lamb wave difference signal tensors

Monitoring in real time and autonomously the health state of aeronautic structures is referred to as structural health monitoring and is a process decomposed in four steps: damage detection, localization, classification, and quantification. In this work, the structures under study are aeronautic geometrically complex structures equipped with a bonded piezoelectric network. When interrogating such a structure, the resulting data lie along three dimensions (namely, the “actuator,”“sensor,” and “time” dimensions) and can thus be interpreted as three-way tensors. The fact that Lamb wave structural health monitoring–based data are naturally three-way tensors is here investigated for damage localization purpose. In this article, it is demonstrated that under classical assumptions regarding wave propagation, the canonical polyadic decomposition of rank 2 of the tensors build from the phase and amplitude of the difference signals between a healthy and damaged states provides direct access to the distances between the piezoelectric elements and damage. This property is used here to propose an original tensor-based damage localization algorithm. This algorithm is successfully validated on experimental data coming from a scale one part of an airplane nacelle (1.5 m in height for a semi circumference of 4 m) equipped with 30 piezoelectric elements and many stiffeners. Obtained results demonstrate that the tensor-based localization algorithm can locate a damage within this structure with an average precision of 10 cm and with a precision lower than 1 cm at best. In comparison with standard damage localization algorithms (delay-and-sum, reconstruction algorithm for probabilistic inspection of defects, and ellipse- or hyperbola-based algorithms), the proposed algorithm appears as more precise and robust on the investigated cases. Furthermore, it is important to notice that this algorithm only takes the raw signals as inputs and that no specific pre-processing steps or finely tuned external parameters are needed. This algorithm is thus very appealing as reliable and easy to settle damage localization timeliness with low false alarm rates are one of the key successes to shorten the gap between research and industrial deployment of structural health monitoring processes

Vibration Energy Harvesting for Wireless Sensors

Kinetic energy harvesters are a viable means of supplying low-power autonomous electronic systems for the remote sensing of operations. In this Special Issue, through twelve diverse contributions, some of the contemporary challenges, solutions and insights around the outlined issues are captured describing a variety of energy harvesting sources, as well as the need to create numerical and experimental evidence based around them. The breadth and interdisciplinarity of the sector are clearly observed, providing the basis for the development of new sensors, methods of measurement, and importantly, for their potential applications in a wide range of technical sectors