scholarly journals APPLICATION OF NON-DESTRUCTIVE TEST FOR STRUCTURAL HEALTH MONITORING - STATE OF THE ART REVIEW

2015 ◽  
Vol 04 (03) ◽  
pp. 105-108 ◽  
Author(s):  
Darshakkumar.V.Mehta .
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
State Of The Art ◽  
Structural Health ◽  
Destructive Test ◽  
Non Destructive

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Chirplet Transform in Ultrasonic Non-Destructive Testing and Structural Health Monitoring: A Review

2019 ◽  
Vol 9 (1) ◽  
pp. 3778-3781
Author(s):  
M. S. Mohammed ◽  
K. Ki-Seong
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Ultrasonic Signal ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Structural Health ◽  
Chirplet Transform ◽  
Testing Signal ◽  
Short Time ◽  
Non Destructive

Ultrasonic non-destructive testing signal can be decomposed into a set of chirplet signals, which makes the chirplet transform a fitting ultrasonic signal analysis and processing method. Moreover, compared to wavelet transform, short-time Fourier transform and Gabor transform, chirplet transform is a comprehensive signal approximation method, nevertheless, the former methods gained more popularity in the ultrasonic signal processing research. In this paper, the principles of the chirplet transform are explained with a simplified presentation and the studies that used the transform in ultrasonic non-destructive testing and in structural health monitoring are reviewed to expose the existing applications and motivate the research in the potential ones.

Development of a Convolutional Neural Network Assisted Fiber Optics Based Passive Structural Health Monitoring System

2021 ◽  
Author(s):  
Ainulla Khan ◽  
Krishnan Balasubramaniam
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Optics ◽  
Health Monitoring ◽  
Alternative Energy ◽  
Power Sources ◽  
Structural Health ◽  
Learning Framework ◽  
Fbg Sensors ◽  
Non Destructive

Abstract The continuous Non-Destructive Evaluation of assets for long-term assurance of performance has led to several developments over the deployment of a Real-Time Structural Health Monitoring (SHM) system. Considering the challenges involved under the implementation of an SHM system for the applications working under harsh environmental conditions with limited access to power sources this work is aimed to contribute towards overcoming those challenges by using the noise from the structure’s machinery or any ambient source as an alternative energy source and employing Fiber Optics based sensing, for its applicability under harsh environments. The required SHM system is realized with the cross-correlation of a fully diffused noise field, sensed using the Fiber Bragg Grating (FBG) sensors at two random locations. With no control on the input received as noise, to this end, a method is developed based on a Deep Learning framework, which is aimed towards a Universal Deployment of the passive SHM system. The methodology is designed to perform the health monitoring of the system, independent of the input perturbations. The validation performed on simulation data has demonstrated the feasibility of the developed technique towards the required kind of passive SHM system.

scholarly journals Machine learning at the interface of structural health monitoring and non-destructive evaluation

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190581
Author(s):  
P. Gardner ◽  
R. Fuentes ◽  
N. Dervilis ◽  
C. Mineo ◽  
S.G. Pierce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Ultrasonic Inspection ◽  
Guided Wave ◽  
Structural Health ◽  
Non Destructive Evaluation ◽  
Ultrasonic Guided Wave ◽  
Detection And Identification ◽  
Non Destructive

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

scholarly journals Structural Health Monitoring (SHM) of Machine Using a Combination of Non-Destructive Testing for Heavy Manufacturing Industry

2020 ◽  
Vol 184 ◽  
pp. 01059
Author(s):  
Ashish Khaira ◽  
Ravi. K. Dwivedi ◽  
Sanjay Jain
Keyword(s):  
Structural Health Monitoring ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Manufacturing Industry ◽  
Research Work ◽  
Cost Benefit ◽  
Life Cycles ◽  
Destructive Testing ◽  
Structural Health ◽  
Non Destructive

Markets are affected by assorted consumer requirements, which insist on superior quality, shorter delivery time, better customer support, and lower prices. Simultaneously, product life cycles are becoming shorter. Success relies on having either a cost-benefit or a value benefit, or, both in any competitive context. Therefore, non-destructive techniques (NDT) become vital but in the conventional system, the maintenance personnel has to visit the machine that consumes time and energy. In the present COVID-19 situation and to save energy and time, there is a necessity of making condition monitoring contactless as much as possible. Therefore, in this research work, a structural health monitoring analysis presented that covers: firstly, enlisting of the NDT infrastructure commonly available in heavy manufacturing industries; secondly, common causes and reasons of machine failures and finally, discusses need of embedded structural health monitoring (e-SHM) system with the combination of NDT in place of existing monitoring practice. The presented work suggested that a combination of NDT with e-SHM is better for timely fault detection to ensure effective condition monitoring.

scholarly journals Real-time structural health monitoring for concrete beams: a cost-effective ‘Industry 4.0’ solution using piezo sensors

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arka Ghosh ◽  
David John Edwards ◽  
M. Reza Hosseini ◽  
Riyadh Al-Ameri ◽  
Jemal Abawajy ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Industry 4.0 ◽  
Concrete Beams ◽  
Low Cost ◽  
Cost Effective ◽  
Content Type ◽  
Structural Health ◽  
Non Destructive

PurposeThis research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems.Design/methodology/approachA mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT).FindingsThe research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners.Originality/valueAccurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

scholarly journals Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3730 ◽  
Author(s):  
Pengcheng Jiao ◽  
King-James I. Egbe ◽  
Yiwei Xie ◽  
Ali Matin Nazar ◽  
Amir H. Alavi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Materials ◽  
State Of The Art ◽  
Piezoelectric Materials ◽  
Great Promise ◽  
Monitoring Systems ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Health Monitoring Systems

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

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