A non-destructive technique for the health monitoring of tie-rods in ancient buildings

2011 ◽  
pp. 7-13 ◽  
Author(s):  
R. Garziera ◽  
L. Collini
Keyword(s):  
Health Monitoring ◽  
Tie Rods ◽  
Non Destructive

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.

Steel Health Monitoring Using Magnetic Techniques

2014 ◽  
Vol 792 ◽  
pp. 139-143 ◽  
Author(s):  
Polykseni Vourna ◽  
Aphrodite Ktena ◽  
A. Mpalliou ◽  
Athanasios G. Mamalis ◽  
Evangelos Hristoforou
Keyword(s):  
Health Monitoring ◽  
Ferromagnetic Materials ◽  
Barkhausen Noise ◽  
Magnetic Response ◽  
Intrinsic Properties ◽  
Magnetic Techniques ◽  
Non Destructive ◽  
Non Destructive Techniques

In the present work the determination of intrinsic properties in ferromagnetic materials by using magnetic non-destructive techniques is discussed. Barkhausen noise and bulk permeability measurements were used in various steels. The monotonic dependence of the magnetic response on microstructural features was verified.

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.

Ultra-violet health monitoring of smart composite laminate using embedded fiber Bragg grating sensors

2020 ◽  
Vol 54 (22) ◽  
pp. 3143-3158 ◽  
Author(s):  
S Mohanta ◽  
Y Padarthi ◽  
S Chokkapu ◽  
J Gupta ◽  
S Neogi
Keyword(s):  
Health Monitoring ◽  
Composite Laminate ◽  
Matrix Material ◽  
Ultra Violet ◽  
Fiber Reinforced Polymer ◽  
Glass Fiber Reinforced Polymer ◽  
Reinforced Polymer ◽  
Glass Fiber Reinforced ◽  
Non Destructive ◽  
Strength Retention

A novel approach is developed to evaluate the property retention on prolonged ultra-violet exposure and hence, health monitoring of glass fiber reinforced polymer composite laminate. This is achieved in a non-destructive manner by mapping the strength retention with the established strain. Embedded fiber Bragg grating sensor and strain gauges are employed to monitor the strain evolution within the laminate. Tensile and flexural tests are conducted at regular intervals to estimate the mechanical strength retention with varying duration of ultra-violet exposure. Through this analysis, it is observed that the property degradation mechanism follows the first-order reaction kinetics. The degradation of matrix material along with the stress relaxation over time develops the stress–strain fields near the interfaces of matrix and fiber. Moreover, the established strain is interpreted by formulating the model that considers the unifying influence of stress relaxation and chemical degradation. This model has closely (R2 = 0.9810 and 0.9790) predicted the experimental data of strain than the existing ones (R2 = 0.9142 and 0.9119). Besides, property retention is mapped with the predicted strain. More importantly, FESEM and FTIR confirm the fact that ultra-violet radiation degrades the matrix material, and thus the mechanical property gets significantly deteriorated. This suggests that the strain measurement is an effective, non-destructive and health monitoring technique to assess the property degradation of the manufactured glass fiber reinforced polymer composites.

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.

Soft matter acoustics: non-destructive health monitoring of polymer blend films

2007 ◽  
Author(s):  
Albert E. Kamanyi ◽  
Wilfred Ngwa ◽  
Tribikram Kundu ◽  
Wolfgang Grill
Keyword(s):  
Polymer Blend ◽  
Health Monitoring ◽  
Soft Matter ◽  
Blend Films ◽  
Non Destructive
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