A Review on Damage Identification and Structural Health Monitoring for Offshore Platform

2016 ◽  
Author(s):  
Liping Sun ◽  
Yang Lu ◽  
Xinyue Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Offshore Platform ◽  
Statistical Pattern Recognition ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Identification Methods ◽  
Structural Health ◽  
Statistical Pattern

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.

scholarly journals Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

2006 ◽  
Vol 13 (4-5) ◽  
pp. 519-530 ◽  
Author(s):  
Charles R. Farrar ◽  
David W. Allen ◽  
Gyuhae Park ◽  
Steven Ball ◽  
Michael P. Masquelier
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Software Package ◽  
Model Development ◽  
General Purpose ◽  
Statistical Pattern Recognition ◽  
Structural Health ◽  
Statistical Pattern ◽  
Statistical Model Development ◽  
Processing Platform

The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discuss each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.

scholarly journals Structural Health Monitoring Applications in Tall Buildings

2020 ◽  
Vol 198 ◽  
pp. 02020
Author(s):  
Yifan Zhao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Tall Buildings ◽  
Modal Parameter ◽  
Index Method ◽  
Structural Dynamic ◽  
Modal Parameter Identification ◽  
Structural Health ◽  
Whole Process ◽  
Neural Network Algorithm

Since there is not much research on structural health monitoring (SHM) applications in tall buildings nowadays, this paper gives a proposal of how it can be applied on skyscrapers. Covering the whole process of SHM, this paper focuses more on the diagnostic algorithms, including Structural dynamic index method, Modal parameter identification method Neural network algorithm and Genetic algorithm and how these algorithms can be used in SHM. After introducing the basic process of SHM, an example is given to show how these principles can be applied in this over 400m building. And after all these introductions, a conclusion can be drawn that the structural health monitoring system can be applied properly in tall buildings following the way proposed in this paper.

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

An introduction to structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 303-315 ◽  
Author(s):  
Charles R Farrar ◽  
Keith Worden
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Technology Development ◽  
Economic Benefits ◽  
Theme Issue ◽  
Structural Health ◽  
Statistical Pattern ◽  
Significant Life ◽  
History Of

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.

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