The Experts below are selected from a list of 48126 Experts worldwide ranked by ideXlab platform
Yuequan Bao - One of the best experts on this subject based on the ideXlab platform.
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Machine learning paradigm for Structural Health Monitoring
Structural Health Monitoring, 2020Co-Authors: Yuequan BaoAbstract:Structural Health diagnosis and prognosis is the goal of Structural Health Monitoring. Vibration-based Structural Health Monitoring methodology has been extensively investigated. However, the conve...
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A novel distribution regression approach for data loss compensation in Structural Health Monitoring
Structural Health Monitoring, 2017Co-Authors: Zhicheng Chen, Yuequan Bao, Billie F. SpencerAbstract:Structural Health Monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all Structural Health Monitoring systems is data loss or data ...
Xiao-jun Deng - One of the best experts on this subject based on the ideXlab platform.
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Applications of Structural Health Monitoring on Intelligent High-speed Train
Structural Health Monitoring-an International Journal, 2015Co-Authors: Jian-ying Liang, Xiao-jun DengAbstract:As a key direction for the technology development of Next Generation Highspeed Train, intelligence will be realized in such critical areas as operation and control, safety and train management, maintenance and repair, as well as passenger service. Structural Health Monitoring technology is an important approach towards the intelligence of train safety, repairing and maintenance. Considering the operational characteristics of the train running at high speed in complex ground environment, a Structural Health Monitoring system is specially developed for highspeed train based on the proven technologies of Structural Health Monitoring in aerospace and energy sectors. This system will play an important role in actualizing the intelligence of Structural state Monitoring, lifespan assessment and conditionbased maintenance. The research of Structural Health Monitoring technology for highspeed train is focused on vibration Monitoring of onboard rotating parts, impact Monitoring of running gear, corrosion, crack and flaw Monitoring of bearing structure, and temperature distribution Monitoring of friction pair structure, etc.. Therefore, Monitoring on the service capability of train structure can be performed in a continuous manner, and the impact of Structural status and service environment on train safety can be evaluated by updating relevant data periodically. doi: 10.12783/SHM2015/254
Irem Velibeyoglu - One of the best experts on this subject based on the ideXlab platform.
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Sensor Placement Optimization for Structural Health Monitoring
Structural Health Monitoring 2015, 2015Co-Authors: Carl Malings, Matteo Pozzi, Irem VelibeyogluAbstract:When designing a system for Structural Health Monitoring, one must decide how to optimally deploy sensor systems to obtain information beneficial to the management of the structure. In this paper, we present a methodology for making these decisions based on a probabilistic graphical model representing the structure to be monitored, as well as actions undertaken by infrastructure managers in Monitoring and maintaining the structure. A value of information metric is used to quantify the benefit of different proposed sensor placement schemes. A greedy algorithm heuristic is used to optimize sensor placements based on this metric. This methodology is demonstrated on an example Structural Health Monitoring problem based on fiber optic strain gauge measurements of the Sherman and Joyce Bowie Scott Hall, under construction on the Carnegie Mellon University campus in Pittsburgh, PA, USA. doi: 10.12783/SHM2015/301
Charles R. Farrar - One of the best experts on this subject based on the ideXlab platform.
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Structural Health Monitoring: A Machine Learning Perspective
2012Co-Authors: Charles R. Farrar, Keith WordenAbstract:This book focuses on Structural Health Monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.
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Nonlinear feature identification of impedance-based Structural Health Monitoring
Smart Structures and Materials 2004: Smart Structures and Integrated Systems, 2004Co-Authors: Amanda C. Rutherford, Gyu Hae Park, H. Sohn, Charles R. FarrarAbstract:The impedance-based Structural Health Monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of Structural Health Monitoring applications. Relying on high frequency local excitations (typically>20 kHz), this technique is very sensitive to minor changes in Structural integrity in the near field of piezoelectric sensors. Several damage sensitive features have been identified and used coupled with the impedance methods. Most of these methods are, however, limited to linearity assumptions of a structure. This paper presents the use of experimentally identified nonlinear features, combined with impedance methods, for Structural Health Monitoring. Their applicability to for damage detection in various frequency ranges is demonstrated using actual impedance signals measured from a portal frame structure. The performance of the nonlinear feature is compared with those of conventional impedance methods. This paper reinforces the utility of nonlinear features in Structural Health Monitoring and suggests that their varying sensitivity in different frequency ranges may be leveraged for certain applications.
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Integrated Structural Health Monitoring
Proceedings of the SPIE - The International Society for Optical Engineering, 2001Co-Authors: Charles R. Farrar, H. Sohn, Michael L Fugate, Joseph J CzarneckiAbstract:Structural Health Monitoring is the implementation of a damage detection strategy for aerospace, civil and mechanical engineering infrastructure. Typical damage experienced by this infrastructure might be the development of fatigue cracks, degradation of Structural connections, or bearing wear in rotating machinery. The goal of the research effort reported herein is to develop a robust and cost-effective Structural Health Monitoring solution by integrating and extending technologies from various engineering and information technology disciplines. It is the author's opinion that all Structural Health Monitoring systems must be application specific. Therefore, a specific application, Monitoring welded moment resisting steel frame connections in structures subjected to seismic excitation, is described along with the motivation for choosing this application. The Structural Health Monitoring solution for this application will integrate Structural dynamics, wireless data acquisition, local actuation, micro-electromechanical systems (MEMS) technology, and statistical pattern recognition algorithms. The proposed system is based on an assessment of the deficiencies associated with many current Structural Health Monitoring technologies including past efforts by the authors. This paper provides an example of the integrated approach to Structural Health Monitoring being undertaken at Los Alamos National Laboratory and summarizes progress to date on various aspects of the technology development
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Structural Health Monitoring of Welded Connections
The First International Conference on Steel & Composite Structures, Pusan, Korea, June 14-16, 2001, 2001Co-Authors: H. Sohn, Charles R. Farrar, Michael L Fugate, Joseph J CzarneckiAbstract:Structural Health Monitoring is the implementation of a damage detection strategy for aerospace, civil and mechanical engineering infrastructure. Typical damage experienced by this infrastructure might be the development of fatigue cracks, degradation of Structural connections, or bearing wear in rotating machinery. The goal of the research effort reported herein is to develop a robust and cost-effective Monitoring system for welded beam-column connections in a moment resisting frame structure. The Structural Health Monitoring solution for this application will integrate Structural dynamics, wireless data acquisition, local actuation, micro-electromechanical systems (MEMS) technology, and statistical pattern recognition algorithms. This paper provides an example of the integrated approach to Structural Health Monitoring being undertaken at Los Alamos National Laboratory and summarizes progress to date on various aspects of the technology development.
Limao Zhang - One of the best experts on this subject based on the ideXlab platform.
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Computational methodologies for optimal sensor placement in Structural Health Monitoring: A review:
Structural Health Monitoring, 2019Co-Authors: Yi Tan, Limao ZhangAbstract:Structural Health Monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement is critical in the Structural Health Monitoring system as the sensor configuration directly impacts the quality of collected data used for Structural Health diagnosis. Therefore, this study presents a comprehensive review of computational methodologies for optimal sensor placement in Structural Health Monitoring. The problem formulation of optimal sensor placement is first introduced, including commonly used evaluation criteria for sensor configurations. Then, various existing optimization methodologies for sensor placement are summarized and introduced in detail, especially for the evolutionary algorithms and their improved variants. Finally, the suitability of computational methods for specific Structural Health Monitoring applications is also discussed. The main goal of this study is to deliver a comprehensive reference of computational methodologies for optimal sensor placement in Structural Health Monitoring studies and applications. This article is concluded by highlighting the most widely utilized evaluation criteria and optimization methodologies for sensor configuration determination.
