The use of intelligent computational tools for damage detection and identification with an emphasis on composites – A review

Gomes, Guilherme Ferreira; Mendéz, Yohan Alí Diaz; Alexandrino, Patrícia da Silva Lopes; Cunha, Sebastião Simões da; Ancelotti, Antônio Carlos

doi:10.1016/j.compstruct.2018.05.002

Cited by 121 publications

(50 citation statements)

References 103 publications

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“…The advantage of the SHM is characterized by being a very promising alternative and efficient with respect to the conventional methods. Indeed, visual inspections are not always possible and are, in any case, expensive in terms of time and money, while the use of conventional methods cannot give up on the operator experience [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

Muttillo

Stornelli

Alaggio

et al. 2020

Sensors

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In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving.

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Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

Muttillo

Stornelli

Alaggio

et al. 2020

Sensors

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“…As a vital material widely used in structure buildings, metal plates are isotropic and have a working condition of high temperature, high pressure and strong noise. To minimize the maintenance costs and to increase the lifetime of these structures, researchers are increasingly interested in improving current nondestructive evaluation (NDE) technologies or building advanced structural health monitoring (SHM) strategies [1,5]. Specifically, SHM schemes are generally applied to the localization and identification of metal plates deficiencies which are commonly fatigue cracks according to AE technology [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Yang

2020

IEEE Access

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Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors' knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a tenfold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5") and 48 mm (1.9"), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5") and 67 mm (2.1"), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with singlesensor. INDEX TERMS acoustic emission (AE); sources localization and identification; stacked denoising autoencoders (SDAE); metallic steel plates; structural health monitoring

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“…Various damage modes exist (e.g., delamination, fiber fracture, and matrix cracking) and their detection and characterization are rather difficult. Delamination is the most common and dangerous failure mode for composites, because it takes place and grows in the absence of any visible surface damage, making it difficult to detect by visual inspection [2,3,4]. Due to the general anisotropic behavior [5] and complex damage scenarios, the successful implementation of delamination detection in aerospace composite structures is always challenging.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based In-Situ Detection and Quantification of Delamination in Composite Plates

Mei

Migot

Haider

et al. 2019

Sensors

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This paper presents a new methodology for detecting and quantifying delamination in composite plates based on the high-frequency local vibration under the excitation of piezoelectric wafer active sensors. Finite-element-method-based numerical simulations and experimental measurements were performed to quantify the size, shape, and depth of the delaminations. Two composite plates with purpose-built delaminations of different sizes and depths were analyzed. In the experiments, ultrasonic C-scan was applied to visualize the simulated delaminations. In this methodology, piezoelectric wafer active sensors were used for the high-frequency excitation with a linear sine wave chirp from 1 to 500 kHz and a scanning laser Doppler vibrometer was used to measure the local vibration response of the composite plates. The local defect resonance frequencies of delaminations were determined from scanning laser Doppler vibrometer measurements and the corresponding operational vibration shapes were measured and utilized to quantify the delaminations. Harmonic analysis of local finite element model at the local defect resonance frequencies demonstrated that the strong vibrations only occurred in the delamination region. It is shown that the effect of delamination depth on the detectability of the delamination was more significant than the size of the delamination. The experimental and finite element modeling results demonstrate a good capability for the assessment of delamination with different sizes and depths in composite structures.

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The use of intelligent computational tools for damage detection and identification with an emphasis on composites – A review

Cited by 121 publications

References 103 publications

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Vibration-Based In-Situ Detection and Quantification of Delamination in Composite Plates

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