High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

Momeni, Hamed; Ebrahimkhanlou, Arvin

doi:10.1088/1361-665x/ac50f4

Cited by 31 publications

(20 citation statements)

References 136 publications

(181 reference statements)

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“…In this way, the present study is a precursor step before crack quantification algorithms. [5][6][7][8][9] The main idea in the current paper is the integration of data collected from depth cameras and LiDARs. Such sensors are becoming ubiquitously available on robotic platforms and VR devices.…”

Section: Introductionmentioning

confidence: 99%

A robotic approach for crack detection through the integration of cameras and LiDARs

Alamdari

Ebrahimkhanlou

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, image processing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.

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Section: Introductionmentioning

confidence: 99%

A robotic approach for crack detection through the integration of cameras and LiDARs

Alamdari

Ebrahimkhanlou

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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“…With the in-depth research into deep learning in rail-crack detection technology, its application in fault detection has diversified. At present, non-destructive testing technology is widely used in railways, and the most commonly applied testing technologies include ultrasonic testing [ 4 ], eddy current testing [ 5 ], appearance testing [ 6 ], etc. Milosevic et al (2022) [ 7 ] presented the simultaneous measurement of sleeper acceleration and six intersecting geometries on the basis of a scan.…”

Section: Introductionmentioning

confidence: 99%

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Kou

Sysyn²,

Fischer³

et al. 2022

Sensors

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Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system’s operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.

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“…In several applications, advancements in technology have resulted in larger and larger data sets in terms of the number of cases and the number of predictors. When the number of predictors is greater than the number of observations, a big problem arises in respect of the estimation of high-dimensional data [10,11]. Some predictors can be redundant, irrelevant, or detrimental to model training in these circumstances.…”

Section: Introductionmentioning

confidence: 99%

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Alweshah

Jebril

Alkhalaileh

et al. 2022

Preprint

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Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.

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High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

Cited by 31 publications

References 136 publications

A robotic approach for crack detection through the integration of cameras and LiDARs

A robotic approach for crack detection through the integration of cameras and LiDARs

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

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