Structural health monitor systems

Chang, F.-K., Markmiller, J.F.C., Yang, J., et al., 2011. Structural Health Monitoring. System Health Management. John Wiley Sons, Ltd, pp. 419—428. 

Bayesian inference Extended Kalman filter Model class selection Model updating Nonpara-metric identification Online updating Outlier, structural health monitoring System identification... 

De Neumann S, Andersen JE, Enckell M, Vullo E (2011) Messina bridge - structural health monitoring system. In Proceedings lABSE-IASS conference. London... 

Damage identification Nondestructive evaluation Structural damage Structural health monitoring System identification... 

There is no damage, and hence, the structural health monitoring system triggered a false alarm. This event has a probability (1 — Pj). 

Qing, X. P. Chan, H.-L. Beard, S. I Kumar, A. 2006. An active diagnostic system for structural health monitoring of rocket engines. /. Intelligent Material Systems and Struct. 17 619-628. 

While conductivities of nanocarbons dispersed in polymers fall short of those of metals, a variety of applications can be unlocked by turning an insulating matrix into a conductor, which requires only small volume fractions that can therefore keep the system viscosity at a level compatible with composite processing techniques. Of particular interest are novel functionalities of these conductive matrices that exploit the presence of a conductive network in them, such as structural health monitoring (SHM) based on changes in electrical resistance of the nanocarbon network as it is mechanically deformed [30]. 

Todd MD, Johnson GA, Vohra ST. Depolyment of a fiber Bragg grating-based measurement system in a structural health monitoring application. Smart Mater Struct 2001 10(3) 534-9. http //dx.doi.Org/10.1088/0964-1726/10/3/316. 

Structural health monitoring (SHM) is a process for evaluation of the structural safety status from structural response measurements. The ultimate goal is to develop an economical and non-destructive system for the earliest possible detection of damage. It has been attracting much attention in the past two decades. A tremendous amount of efforts have been dedicated in this area [15,18,19,25,51,52,68,69,77,101,103,106,138,142,143,149,160,168,174,185], [186,195,201,204,228,238,244,246,261,267,268,281,282]. Comprehensive literature reviews on the development of SHM can be found in Doebling et al. [69] and Sohn et al. [247] and there were also a number of workshops, [1,44,187], for example, and special issues of journals, e.g.. Journal of Engineering Mechanics in July 2000 [90] and January 2004 [26] and Computer-Aided Civil and Infrastructure Engineering in January 2001 [278] and May 2006 [22]. 

If the mathematical model for the system of concern has too many uncertain parameters, the measurement will not provide sufficient mathematical constraints/equations to uniquely identify the uncertain parameters. However, experienced engineers can identify the critical substructures for monitoring. Then, a free body diagram can be drawn to focus on these critical substructures only. Note that the internal forces on the boundary of the substructures are unknown and difficult to measure, so they are treated as an uncertain input to the substructure. Furthermore, these internal forces share the dominant frequencies of the structure so they cannot be modeled arbitrarily as white noise or other prescribed colored noise. However, with the same idea as in Yuen and Katafygiotis [294], these interface forces can be treated as unknown inputs without assuming their time-frequency content [289]. This enables a large number of possible applications in structural health monitoring and also enhances the computational efficiency since one does not need to consider the whole system. 

Keywords Bayesian inference damage detection eigenvalue problem iterative algorithm modal data mode shape expansion model updating structural health monitoring substmcture system identification... 

In this chapter, a Bayesian model updating method using incomplete modal data is presented with applications to structural health monitoring. As reported in the literature [18,51,52,267], the realistic assumption is made that only the modal frequencies and partial mode shapes of some modes are measured system mode shapes are also introduced, which avoid mode matching between the measured modes and those of the dynamical model. The novel feature... 

In the next section, the proposed updating approach is presented which provides estimates of the system modal frequencies and system mode shapes, as well as estimates of the stiffness model parameters, based on incomplete modal data. Examples with a twelve-story building and a three-dimensional braced frame wiU be used to demonstrate the method with applications to structural health monitoring. 

Farrar and Lieven (2006) define damage prognosis as the estimate of an engineered system s remaining useful life . They also introduce the concepts usage monitoring and structural health monitoring. 

We expect the book to be a valuable resource for researchers, including graduate students, in the areas of smart materials and robotics. Practitioners in biomedical engineering, robotic systems, structural health monitoring will also find this book of interest. In addition, the book can serve as a reference for graduate-level courses in smart materials. A brief outline of the book follows. 

It appears that smart structures offer a potential solution for continuous structural health monitoring. A smart stmctnre is defined as a system that is designed for a specific functional purpose, and that operates at a higher level of performance than its conventional counterpart in fulfilling this pnrpose. The system senses its internal state and external, and based on information obtained makes decisions and responds to meet the functional reqnirements [293]. 

Ciang CC, Lee JR, Bang HJ. Structural health monitoring for a wind turbine system a review of damage detection methods. Meas Sci Technol 2008 19(12) 122001. 

Structural health monitoring of civil infrastructure systems... 

Peeters, B., Maeck, J. De Roeck, G. 2000. Excitation sources and dynamic system identification in civil engineering. Proceedings of the European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain, 341-350. 

Deraemaeker, A., Reynders, E., De Roeck, G., Kullaa, J. 2008. Vibration based Structural Health Monitoring using output-only measurements under changing environment. Mechanical Systems and Signal Processing, submitted. 

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