Structural Health Monitoring (SHM) and Determination of Surface Defects in Large Metallic Structures using Ultrasonic Guided Waves

126

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.

Section: Ultrasonic Guided Waves (Ugw)mentioning

confidence: 99%

Section: Ultrasonic Guided Waves (Ugw)mentioning

confidence: 99%

mentioning

confidence: 99%

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Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Kralovec

Schagerl

2020

126

“…In the published literature on damages in laminated composites, the presence of delamination had been assessed from the structural vibration and modal parameters [36], whereas guided waves and acoustic emission had been used for the detection and localization of delamination [37,38]. However, some practical limitations of the methods that employ high-frequency waves for nondestructive evaluation are their need of too many sensors, optimized location of the receivers, the damage to be between the actuator(s) and sensor(s), data acquisition at a higher rate, and complex signal processing [39][40][41]. On the contrary, structural vibration is more readily available for the assessment of damage due to its easy measurement via smart materials [42].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Khan

Shin

Lim

et al. 2020

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

“…Metal materials are widely used in plate-like structures. These structures are required to be sufficiently durable and safe in harsh and complicated environments [1][2][3]. Due to factors such as aging and fatigue, damage gradually accumulates in the structure.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Phase Coherence Imaging for Near-Field Defects by Ultrasonic Phased Array Inspection

Zhang

Zeng

Fan

et al. 2020

This paper describes an imaging method for near-field defect detection in aluminum plates based on Green’s function recovery and application of instantaneous phase coherence weighting factors. The directly acquired acoustic information of near-field defects is usually obscured by the nonlinear effects due to the physical limitation of the acquisition system. Using the diffuse field to recover the Green’s function can effectively retrieve the early time information. However, averaging operations of finite number in this process produces an imperfect imaging result. In order to improve the image quality, two kinds of instantaneous phased coherence weighting factors are used to weight the Green’s function to reduce the background noise and improve the signal-to-noise ratio: the instantaneous phase coherence factor (IPCF), and the instantaneous phase weighting factor (IPWF). Experiments are conducted on two aluminum plates with two and four near-field defects, respectively. As a result, the background noise of amplitude images weighted by IPCF and IPWF is less than that of the conventional total focusing method (TFM). In addition, the IPCF image achieves a better signal-to-noise ratio (SNR) than that of IPWF, and the phase discontinuity in an IPWF image is suppressed through the IPCF.