Structural health monitoring

About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.

Structural health monitoring: Closing the gap between research and industrial deployment:

Abstract: There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there ar...

Flexible smart sensor framework for autonomous structural health monitoring

Abstract: Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of full- scale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

Fiber grating sensors in medicine: Current and emerging applications

Abstract: Fiber grating sensors hold immense potential for biomedical applications due to their inherent properties like small size, biocompatibility, non-toxicity, chemical inertness and electromagnetically inert nature. Grating based sensors are well known technology for structural health monitoring (SHM) in the arena of civil and aerospace; however investigations for their use in the field of medicine are fairly recent and they have not been yet commercialized for the same. As these sensors detect strain, temperature, pressure, vibration, curvature and refractive index of the surrounding material even in high magnetic and electric field environments, they can serve diagnostic purposes in diverse areas of healthcare e.g. biomechanics, cardiology, gynecology, very low temperature monitoring and immunosensing to name a few. Most importantly, they can be used efficiently for thermal and pressure mapping even during MRI procedure where conventional sensors may fail. The paper is a review of various application areas of fiber grating based devices, the status of precedent and ongoing research around the world, and discussion on the issues hampering their rapid growth in the field.

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

Abstract: 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.

Structural health monitoring system based on diffracted Lamb wave analysis by multiresolution processing

Abstract: A health monitoring system is presented composed of integrated disc-shaped, 100 µm thick and 5 mm diameter piezoelectric transducers (PZTs) working sequentially as Lamb wave emitters and receivers. The diagnostic is based on the analysis of Lamb wave signals recorded before and after damage. In the composite, delaminations are discontinuities producing mode conversion processes generating various outgoing modes. The multiresolution processing allows the isolation of various propagation modes and their extraction in order to measure, for various propagation paths, the time delay between the arrivals of the main burst and of a specific outgoing mode. This process permits, with good accuracy, the localization of damage and the estimation of its extent. The robustness and portability of this technique is demonstrated by the fact that, after validation in our laboratory, it was successfully applied to data coming from an experiment conducted in another laboratory using its own acousto-ultrasonic health monitoring hardware system.