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.

Ultrasonic Structural Health Monitoring Using Fiber Bragg Grating.

Abstract: The fiber Bragg grating (FBG) sensor, which was developed over recent decades, has been widely used to measure manifold static measurands in a variety of industrial sectors. Multiple experiments have demonstrated its ability in ultrasonic detection and its potential in ultrasonic structural health monitoring. Unlike static measurements, ultrasonic detection requires a higher sensitivity and broader bandwidth to ensure the fidelity of the ultrasonic Lamb wave that propagates in a plate-like structure for the subsequent waveform analysis. Thus, the FBG sensor head and its corresponding demodulation system need to be carefully designed, and other practical issues, such as the installation methods and data process methods, should also be properly addressed. In this review, the mature techniques of FBG-based ultrasonic sensors and their practical applications in ultrasonic structural health monitoring are discussed. In addition, state-of-the-art techniques are introduced to fully present the current developments.

Enabling framework for structural health monitoring using smart sensors

Abstract: Structural health monitoring (SHM) is an important tool for the ongoing maintenance of aging infrastructure. The ultimate goals of implementing an SHM system are to improve infrastructure maintenance, increase public safety, and minimize the economic impact of an extreme loading event by streamlining repair and retrofit measures. Networks of wireless smart sensors offer tremendous promise for accurate and continuous structural monitoring using a dense array of inexpensive sensors; however, hurdles still remain. Although smart sensors have been commercially available for nearly a decade, full-scale implementation for civil infrastructure has been lacking, with the exception of a few short-term demonstration projects. This slow progress is due in part to the fact that programming smart sensors is extremely complex, putting the use of these devices for all but the simplest tasks out of the reach of most engineers. This article presents an enabling, open-source framework for SHM using networks of wireless smart sensors. The framework is based on a service-oriented architecture that is modular, reusable, and extensible, thus allowing engineers to more readily realize the potential of smart sensing technology. To demonstrate the efficacy of the proposed framework, an example SHM application is provided. Copyright © 2010 John Wiley & Sons, Ltd.

Measurement of strain and bending deflection of a wind turbine tower using arrayed FBG sensors

Abstract: This paper introduces a fiber Bragg grating (FBG) based arrayed sensor system for use in the measurement of strain and bending deflection of an 1.5 MW wind turbine tower, and describes the results of field tests of structural monitoring of turbine start and feathering load conditions. A wavelength division multiplexing (WDM) FBG interrogator was developed with a spectrometer-type demodulator based on a linear photo detector for high-speed strain sensing. Real-time shape estimation of the wind turbine tower was accomplished using strain data gathered by surface mounted fiber Bragg grating sensors. The finite element model of the wind turbine tower was created and the displacement-strain transformation (DST) matrix on the basis of the modal approach was obtained. To monitor the dynamic structural behavior of the wind turbine, 10 FBG sensors were arrayed and installed on the inner surface of the tower located at the primary wind direction. The time histories of the strain were gathered using the FBG sensors and the deflections of the tower top position were simultaneously transformed using the DST matrix. Finally, the full deflection shapes of the tower were successfully estimated using arrayed FBG sensors.

Aspects of structural health and condition monitoring of offshore wind turbines.

Abstract: Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.