Optimal sensors layout design based on reference‐free damage localization with lamb wave propagation

Abstract

Summary This study presents a new approach for designing optimal sensors layout based on accuracy of defect mapping. It is obtained from combination of the reference‐free damage detection technique and the probability‐based diagnostic imaging method. Considering damage indices based on continuous wavelet transform of sensors signals, the core of this study involves with development of a database of continuous wavelet transform features of a crack. In fact, the database contains the data from 594 different states in crack positions, orientations, and the considered sensing path lengths. Eventually, this database is used for localization of damage by interpolating the stored data collected from the above mentioned 594 states of the crack. The verification tests of defect mapping interpolating the stored data show an excellent match with the finite element method crack positioning performed for three different crack conditions. To generalize the results, here, 134 sensor layouts are considered in each of which 121 crack positions are detected by the help of probability‐based diagnostic imaging method. Then to check upon the accuracy of the results, the distance between the real center of the crack and the detected crack position by the interpolation of the stored data is calculated. Finally, the optimal sensor layouts single out from among the 134 ones based on user‐defined accuracy in localizing damage on an arbitrary monitoring area and the number of sensors.

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

“…The guided wave propagation method has been extensively used for the characterization of different types of materials and structures and damage detection purposes. [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] For example, Dahmen et al 32 investigated the elastic properties of olive wood plates using the Lamb and bulk ultrasonic wave propagation methods. Mardanshahi et al 38 used the Lamb wave propagation and artificial intelligence methods to propose an intelligent model for the detection and classification of the matrix cracking in polymer composites.…”

“…Besides, different Lamb wave modes and excitation frequencies can be utilized for the detection of various types of defects and damage with different sizes and geometry. The guided wave propagation method has been extensively used for the characterization of different types of materials and structures and damage detection purposes 32–48 . For example, Dahmen et al 32 investigated the elastic properties of olive wood plates using the Lamb and bulk ultrasonic wave propagation methods.…”

Optimal sensor and actuator placement for structural health monitoring via an efficient convex cost-benefit optimization

“…by selecting the areas with the largest wave amplitude [17]. Their main limitation, however, lies in the computational cost, which can be alleviated if approximation techniques are adopted such as the continuous wavelet transform [18]. However, their optimization procedure relies on combinatorial algorithms such as genetic algorithms [19,20], simulated annealing [21,22], or particle swarm optimization [23] and may, in general, give a suboptimal layout with no indication of its accuracy.…”

Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals

“…In recent years, Lamb-wave-based imaging methods such as phased array [15,16] and delay-and-sum [17][18][19] algorithms have been widely used for damage localization in plate-like structures. The damage imaging methods have been further empowered by probabilistic approaches such as Probability-Based Diagnostic Imaging (PDI) [20][21][22], Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) [23,24], Algebraic Reconstruction Techniques (ART), and Lamb Wave Tomography (LWT) [25,26]. These methods have used different parameters to define the probability regions for individual sensing paths, which were then used to reconstruct the damage images.…”

“…These methods have used different parameters to define the probability regions for individual sensing paths, which were then used to reconstruct the damage images. For example, the PDI methods have used the weight distribution functions [20,21,[27][28][29][30] to define the probability region between actuator-sensor pairs. These distribution functions depended on a scale/shape factor, β, which controlled the effective size of the elliptical probability area for each sensing path.…”