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Sensors Fusion in Non-Destructive Testing Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 37858

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Special Issue Editors

Dr. Nico P. Avdelidis

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Guest Editor

1. Department of Aeronautics and Astronautics, School of Engineering, University of Southampton, Southampton SO16 7QF, UK
2. Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
Interests: NDT; health diagnostics; non-invasive imaging; autonomous systems inspections; composites; structures; thermal imaging; monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Wenbo Duan

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Guest Editor

Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK
Interests: Acoustics; finite element method; non-destructive testing

Prof. Dr. Tat-Hean Gan

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Guest Editor

Brunel Innovation Centre, Brunel University London, Uxbridge, UK
Interests: ultrasonic guided waves; non-destructive testing; artificial intelligence; non-contact ultrasonics; Industry 4.0; signal processing; sensors; instrumentations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) is an engineering approach for examining the properties of a structure or system without causing damage. This technology is widely used in medical imaging, mechanical engineering, civil engineering, etc. While it is relatively straightforward to inspect a small component or structure non-destructively in many applications, non-destructive testing of large structures, such as wind turbine blades, long distance pipelines, railways, etc., is often extremely challenging. This generally requires sensors fusion from the various non-destructive testing technologies applied.

This Special Issue encourages advances in sensors fusion in non-destructive testing and evaluation applications associated with (but not limited to) ultrasonic, eddy-current, thermography, and shearography techniques. We would like to invite original research articles, as well as review articles, that contain theoretical, analytical, and experimental investigations covering all aspects of NDT&E sensing. Applications associated with large structures are particularly welcome.

Prof. Dr. Nicolas P. Avdelidis
Dr. Wenbo Duan
Prof. Dr. Tat-Hean Gan
Guest Editors

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Keywords

Nondestructive
Structures
Sensors
Imaging
Structural health monitoring.

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Published Papers (7 papers)

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Research

14 pages, 7361 KiB

Open AccessArticle

Novel Electromagnetic Sensors Embedded in Reinforced Concrete Beams for Crack Detection

by Michaela Gkantou, Magomed Muradov, George S. Kamaris, Khalid Hashim, William Atherton and Patryk Kot

Sensors 2019, 19(23), 5175; https://doi.org/10.3390/s19235175 - 26 Nov 2019

Cited by 79 | Viewed by 5130

Abstract

This paper investigates the possibility of applying novel microwave sensors for crack detection in reinforced concrete structures. Initially, a microstrip patch antenna with a split ring resonator (SRR) structure was designed, simulated and fabricated. To evaluate the sensor’s performance, a series of structural tests were carried out and the sensor responses were monitored. Four reinforced concrete (RC) beam specimens, designed according to the European Standards, were tested under three-point bending. The load was applied incrementally to the beams and the static responses were monitored via the use of a load cell, displacement transducers and crack width gauges (Demec studs). In parallel, signal readings from the microwave sensors, which were employed prior to the casting of the concrete and located along the neutral axis at the mid-span of the beam, were recorded at various load increments. The microwave measurements were analysed and compared with those from crack width gauges. A strong linear relationship between the crack propagation and the electromagnetic signal across the full captured spectrum was found, demonstrating the technique’s capability and its potential for further research, offering a reliable, low-cost option for structural health monitoring (SHM). Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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10 pages, 2950 KiB

Open AccessArticle

Investigation of a Magnetic Tunnel Junction Based Sensor for the Detection of Defects in Reinforced Concrete at High Lift-Off

by Muhamad Arif Ihsan Mohd Noor Sam, Zhenhu Jin, Mikihiko Oogane and Yasuo Ando

Sensors 2019, 19(21), 4718; https://doi.org/10.3390/s19214718 - 30 Oct 2019

Cited by 10 | Viewed by 3700

Abstract

Magnetic flux leakage (MFL) testing is a method of non-destructive testing (NDT), whereby the material is magnetized, and when a defect is present, the magnetic flux lines break out of the material. The magnitude of the leaked magnetic flux decreases as the lift-off (distance from the material) increases. Therefore, for detection at high lift-off, a sensitive magnetic sensor is required. To increase the output sensitivity, this paper proposes the application of magnetic tunnel junction (MTJ) sensors in a bridge circuit for the NDT of reinforced concrete at high lift-off. MTJ sensors were connected to a full-bridge circuit, where one side of the arm has two MTJ sensors connected in series, and the other contains a resistor and a variable resistor. Their responses towards a bias magnetic field were measured, and, based on the results, the sensor circuit sensitivity was 0.135 mV/mT. Finally, a reinforced concrete specimen with a 1 cm gap in the center was detected. The sensor module (with an amplifier and low pass filter circuits) could determine the gap even at 50 cm, suggesting that MTJ sensors have the potential to detect defects at high lift-off values and have a promising future in the field of NDT. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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12 pages, 3910 KiB

Open AccessArticle

Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network

by M. M. Manjurul Islam and Jong-Myon Kim

Sensors 2019, 19(19), 4251; https://doi.org/10.3390/s19194251 - 30 Sep 2019

Cited by 103 | Viewed by 7771

Abstract

The visual inspection of massive civil infrastructure is a common trend for maintaining its reliability and structural health. However, this procedure, which uses human inspectors, requires long inspection times and relies on the subjective and empirical knowledge of the inspectors. To address these limitations, a machine vision-based autonomous crack detection method is proposed using a deep convolutional neural network (DCNN) technique. It consists of a fully convolutional neural network (FCN) with an encoder and decoder framework for semantic segmentation, which performs pixel-wise classification to accurately detect cracks. The main idea is to capture the global context of a scene and determine whether cracks are in the image while also providing a reduced and essential picture of the crack locations. The visual geometry group network (VGGNet), a variant of the DCCN, is employed as a backbone in the proposed FCN for end-to-end training. The efficacy of the proposed FCN method is tested on a publicly available benchmark dataset of concrete crack images. The experimental results indicate that the proposed method is highly effective for concrete crack classification, obtaining scores of approximately 92% for both the recall and F1 average. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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18 pages, 6141 KiB

Open AccessArticle

Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network

by Shucong Liu, Dezhi Zheng and Rui Li

Sensors 2019, 19(17), 3740; https://doi.org/10.3390/s19173740 - 29 Aug 2019

Cited by 15 | Viewed by 5177

Abstract

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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16 pages, 2801 KiB

Open AccessArticle

An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

by Yongbo Li, James Xi Gu, Dong Zhen, Minqiang Xu and Andrew Ball

Sensors 2019, 19(9), 2205; https://doi.org/10.3390/s19092205 - 13 May 2019

Cited by 42 | Viewed by 5920

Abstract

As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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19 pages, 5740 KiB

Open AccessArticle

A Non-Destructive Testing Method for Fault Detection of Substation Grounding Grids

by Xiujuan Wang, Zhihong Fu, Yao Wang, Renkuan Liu and Lin Chen

Sensors 2019, 19(9), 2046; https://doi.org/10.3390/s19092046 - 2 May 2019

Cited by 20 | Viewed by 3860

Abstract

The grounding grid is critical to the safety and stability of a power system. Corrosive cracking of the grounding conductor is the main cause of deterioration of grounding grid performance. Existing fault diagnosis methods for grounding grids are limited by the number and distribution of grounding leads, and some of them cannot be used for online detection. This paper proposes a grounding grid detection method based on magnetic source excitation. The measuring device consists of four coils, two horizontal excitation coils, and two vertical receiving coils. The secondary magnetic field signal is extracted from the primary field and the background field by properly positioning the coils, such that the measured signal can reflect the underground media more accurately. The measuring device of the method is portable, the measurement process is contactless with the grounding grid, and it is not limited by the grounding leads. Furthermore, it has a strong anti-interference ability and can realize online detection. It was proven by simulations and experiments that the proposed method has a higher measurement accuracy and stronger anti-interference ability when compared with existing methods. This paper also discusses the influence of various factors such as the number and the location of the breakpoints, the frequency of the excitation source, the soil resistivity, and stratification from the measurement data. It was proven that the method has high precision and a wide application range, and is important for guiding significance and reference value in engineering applications. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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12 pages, 14050 KiB

Open AccessArticle

Inspection of Aircraft Wing Panels Using Unmanned Aerial Vehicles

by Vasileios Tzitzilonis, Konstantinos Malandrakis, Luca Zanotti Fragonara, Jose Angel Gonzalez Domingo, Nicolas P. Avdelidis, Antonios Tsourdos and Kevin Forster

Sensors 2019, 19(8), 1824; https://doi.org/10.3390/s19081824 - 17 Apr 2019

Cited by 16 | Viewed by 5278

Abstract

In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects’ detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects’ detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects. Full article

(This article belongs to the Special Issue Sensors Fusion in Non-Destructive Testing Applications)

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