An introduction to structural health monitoring

Author(s):  
Charles R Farrar ◽  
Keith Worden

The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

Author(s):  
Liping Sun ◽  
Yang Lu ◽  
Xinyue Zhang

Structural health monitoring (SHM) based on vibration measurements in large/complex structures were shown to be promising by researchers. The authors believe that the SHM problem is fundamentally one of statistical pattern recognition. Therefore, the damage detection studies reviewed herein are summarized in the context of a statistical pattern recognition paradigm[1]. This paradigm can be described as a three-part process: (1) Data acquisition and cleansing, (2) Modal parameter identification, (3) Damage identification methods. However, offshore platform structures are very complex, and not easy to excite artificially and they are often suffered from ambient loads that cannot be controlled easily. The thesis focuses on three key issues for structural health monitoring via vibration in real offshore platform structures[2]. In the first part of review, the offshore platform structure health monitoring system basic principle and the composition are discussed. In the second portion, three important processes of structure health monitoring are summarized (Data acquisition and cleansing, modal parameter identification, damage identification methods), and each method good and bad points is pointed out. Next, Application of damage identification and structural health monitoring to offshore platform are in detail produced, the methods are described in general terms including difficulties associated with their implementation. Finally, current and future-planned applications of this technology to offshore platform are summarized. The paper concludes with a discussion of critical issues for future research on damage identification and structural health monitoring for offshore platform.


2021 ◽  
Vol 6 (5) ◽  
pp. 1107-1116
Author(s):  
Tingna Wang ◽  
David J. Wagg ◽  
Keith Worden ◽  
Robert J. Barthorpe

Abstract. Structural health monitoring (SHM) is often approached from a statistical pattern recognition or machine learning perspective with the aim of inferring the health state of a structure using data derived from a network of sensors placed upon it. In this paper, two SHM sensor placement optimisation (SPO) strategies that offer robustness to environmental effects are developed and evaluated. The two strategies both involve constructing an objective function (OF) based upon an established damage classification technique and an optimisation of sensor locations using a genetic algorithm (GA). The key difference between the two strategies explored here is in whether any sources of benign variation are deemed to be observable or not. The relative performances of both strategies are demonstrated using experimental data gathered from a glider wing tested in an environmental chamber, with the structure tested in different health states across a series of controlled temperatures.


2018 ◽  
Vol 95 ◽  
pp. 1-13 ◽  
Author(s):  
Mario A. de Oliveira ◽  
Nelcileno V.S. Araujo ◽  
Daniel J. Inman ◽  
Jozue Vieira Filho

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.


Author(s):  
R. Fuentes ◽  
E.J. Cross ◽  
P.A. Gardner ◽  
L.A. Bull ◽  
T.J. Rogers ◽  
...  

2013 ◽  
Vol 569-570 ◽  
pp. 628-635 ◽  
Author(s):  
Jonas Falk Skov ◽  
Martin Dalgaard Ulriksen ◽  
Kristoffer Ahrens Dickow ◽  
Poul Henning Kirkegaard ◽  
Lars Damkilde

The aim of the present paper is to provide a state-of-the-art outline of structural health monitoring (SHM) techniques, utilizing temperature, noise and vibration, for wind turbine blades, and subsequently perform a typology on the basis of the typical 4 damage identification levels in SHM. Before presenting the state-of-the-art outline, descriptions of structural damages typically occurring in wind turbine blades are provided along with a brief description of the 4 damage identification levels.


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