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

Abstract

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.

Damage severity assessment in composite structures using multi-frequency lamb waves

“…In the present work, the damage is emulated by placing permanent magnets on both sides of the plate. [37][38][39] The width of the magnet is 20 mm, and its length is varied between 10 mm and 50 mm with an increment of 10 mm by stacking the magnets. Thus, we effectively introduce discontinuity of varying degrees and observe its effect on the Lamb wave propagation.…”

Informative Bayesian Tools for Damage Localisation by Decomposition of Lamb Wave Signals

“…The full-field multi-mode stage is summarised in Algorithm 1. Reconstruct time-distance data for individual modes using equation (10) and store in u * (g, :, :); 11 end…”

“…There has been evidence of useful localisation techniques that do not require decomposition, such as via the use of piezoelectric rosettes [9]; however, the cost of using such hardware may quickly rise for large-scale systems. A novel method has been proposed by Rebillat et al, involving the decomposition of three-way tensors constructed from 'actuator', 'sensor' and 'time dimensions' that shows robustness [10]. However, this method requires that data are collected from multiple actuation sources and sensor locations, so it may be advantageous in some systems to be able to localise using fewer such locations, thus reducing data usage and processing time.…”

Dichotomy property of dispersion equation of guided waves propagating in anisotropic composite plates

“…SHM algorithms however heavily rely on computational methods dedicated to dispersion curves prediction [4][5][6][7][8][9][10] allowing to link the phase velocity of a given mode with the excitation frequency. Dispersion curves are for example the basis for selecting appropriate excitation frequencies and designing an optimal piezoelectric transducer (PZT) network (position and size of the PZT elements) [11,12] or for advanced damage localization algorithms [13,14]. Accurately and efficiently computing dispersion curves is thus mandatory for the deployment of SHM to aerospace composite structures by means of Lamb waves.…”