Relevant bibliographies by topics / Structural analysis (Engineering) Structural health monitoring

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Structural analysis (Engineering) Structural health monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Structural analysis (Engineering) Structural health monitoring"

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Wang, Xin, and Wei Bing Hu. "Structural Health Monitoring for Steel Structures." Applied Mechanics and Materials 351-352 (August 2013): 1088–91. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1088.

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The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring. Many different types and degrees accidents take place, especially some important collapse accidents, the significance of steel structural health monitoring has been recognized. The introduction begins with a brief research status of steel structural health monitoring in china and the world. The paper analyzes the projects and contents of steel structures monitoring from nine aspects and summarizes the diagnosis methods of steel structural damages which include power fingerprint analysis, the methods of model correction and system identification, neural network methods, genetic algorithm and wavelet analysis, it provides us theoretical guidence. In conclusion, structural health monitoring for steel structures could reduce the impact of such disasters immediately after natural hazards and man-made disasters both economically and socially, thus it is becoming increasingly important.
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Rajic, Nik, Steve C. Galea, and David Rowlands. "Thermoelastic Stress Analysis - Emerging Opportunities in Structural Health Monitoring." Key Engineering Materials 558 (June 2013): 501–9. http://dx.doi.org/10.4028/www.scientific.net/kem.558.501.

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The emergence recently of a thermoelastic stress analysis (TSA) capability exploiting low-cost, compact and rugged microbolometer detector technology provides a significant opportunity topromote a broader use of this powerful non-contact full-field stress analysis technique. An area whereit has considerable and hitherto unexplored potential is in in-situ structural health monitoring (SHM).The present paper outlines the case for a nexus between SHMand TSA in this new form. It is proposedthat the approach should yield diagnostic and prognostic capabilities surpassing those of some existingSHM modalities. An F/A-18 centre-fuselage full-scale structural fatigue test is employed as a casestudy to illustrate the practical feasibility of the approach and to underscore some of its potential.Although the case study focuses on an aircraft structure, the concept has potential application to awide variety of different engineering assets across the aerospace, civil and maritime sectors.
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Krüger, Markus, Christian U. Grosse, and Pedro José Marrón. "Wireless Structural Health Monitoring Using MEMS." Key Engineering Materials 293-294 (September 2005): 625–34. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.625.

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So far, the inspection of building structures and especially of bridges is mainly done visually. Therefore, the condition of the structure is examined from the surface and the interpretation and assessment is based on the experience of the expert. However, the main purpose of monitoring civil structures is not to substitute visual inspection. Continuous structural health monitoring should provide data from the inside of a structure to better understand its structural performance and to predict its durability and remaining life time. Monitoring should render objective data and observable alterations in the structure continuously, which cannot be done by visual inspection. More detailed information is needed with respect to different exposure due to dynamic and static loads and also temperature and moisture. Today mainly wired monitoring systems are used to monitor structures, which are relatively expensive and time consuming to install. In this paper the basic principle of a wireless monitoring system equipped with MEMS sensors is presented, which can be easily installed at different structures. Microelectromechanical systems (MEMS) are small integrated devices or systems that combine electrical and mechanical components. A wireless monitoring sensor network equipped with such MEMS could be produced with a very low budget and becomes very efficient. This permits a wide area of applications not only in civil engineering. With respect to different applications relevant properties of a wireless monitoring system are described. In detail network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction as well as reliability and robustness are discussed.
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Li, Qiang, Chun Xiao, Wei Li, Li Qiao Li, and Hui Liu. "Research on Data Correlation in Structural Health Monitoring System." Advanced Materials Research 671-674 (March 2013): 2044–48. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.2044.

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In structural health monitoring system, data analysis is one of the most important parts. It is mainly for processing and analysis of the collected data, among of which, the correlation analysis of the collected data can be used to verify the feasibility of the system. This paper applies the method of wavelet de-noising analysis to reduce signal noise and utilizes MATLAB and LabVIEW to calculate the cross-correlation coefficient in simulation statistics independently. To verify the feasibility of correlation analysis method and the data processing, simulation study is finished based on sampled data, which are the information of measuring points of strain and temperature from the Baishazhou Bridge. The cross-correlation coefficients between various signals can provide the reference to the whole health status of the civil engineering structure, and then enhance the accuracy of structural health assessment.
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Zeng, Lei, Yiping Liu, Ge Zhang, Liqun Tang, Zhenyu Jiang, and Zejia Liu. "Analysis of structural responses of bridges based on long-term structural health monitoring." Mechanics of Advanced Materials and Structures 25, no. 1 (October 3, 2016): 79–86. http://dx.doi.org/10.1080/15376494.2016.1243283.

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Mykola Sysyn, Olga Nabochenko, Franziska Kluge, Vitalii Kovalchuk, and Andriy Pentsak. "Common Crossing Structural Health Analysis with Track-Side Monitoring." Communications - Scientific letters of the University of Zilina 21, no. 3 (August 15, 2019): 77–84. http://dx.doi.org/10.26552/com.c.2019.3.77-84.

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Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.
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Webb, G. T., P. J. Vardanega, P. R. A. Fidler, and C. R. Middleton. "Analysis of Structural Health Monitoring Data from Hammersmith Flyover." Journal of Bridge Engineering 19, no. 6 (June 2014): 05014003. http://dx.doi.org/10.1061/(asce)be.1943-5592.0000587.

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Lu, Ping, Brent M. Phares, Lowell Greimann, and Terry J. Wipf. "Bridge Structural Health–Monitoring System Using Statistical Control Chart Analysis." Transportation Research Record: Journal of the Transportation Research Board 2172, no. 1 (January 2010): 123–31. http://dx.doi.org/10.3141/2172-14.

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da Silva, Samuel, Milton Dias Júnior, and Vicente Lopes Junior. "Structural Health Monitoring in Smart Structures Through Time Series Analysis." Structural Health Monitoring: An International Journal 7, no. 3 (July 21, 2008): 231–44. http://dx.doi.org/10.1177/1475921708090561.

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Yang, Jian-Ping, Wei-Zhong Chen, Ming Li, Xian-Jun Tan, and Jian-xin Yu. "Structural health monitoring and analysis of an underwater TBM tunnel." Tunnelling and Underground Space Technology 82 (December 2018): 235–47. http://dx.doi.org/10.1016/j.tust.2018.08.053.

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Dissertations / Theses on the topic "Structural analysis (Engineering) Structural health monitoring"

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Lajnef, Nizar. "Self-powered sensing in structural health and usage monitoring." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (Ph.D.)--Michigan State University. Dept. of Civil and Environmental Engineering, 2008.
Title from PDF t.p. (viewed on July 2, 2009) Includes bibliographical references (p. 127-133). Also issued in print.
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Singh-Levett, Ishan. "Real-time integral based structural health monitoring." Thesis, University of Canterbury. Mechanical Engineering, 2006. http://hdl.handle.net/10092/1171.

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Structural Health Monitoring (SHM) is a means of identifying damage from the structural response to environmental loads. Real-time SHM offers rapid assessment of structural safety by owners and civil defense authorities enabling more optimal response to major events. This research presents an real-time, convex, integral-based SHM methods for seismic events that use only acceleration measurements and infrequently measured displacements, and a non-linear baseline model including hysteretic dynamics and permanent deformation. The method thus identifies time-varying pre-yield and post-yield stiffness, elastic and plastic components of displacement and final residual displacement. For a linear baseline model it identifies only timevarying stiffness. Thus, the algorithm identifies all key measures of structural damage affecting the immediate safety or use of the structure, and the long-term cost of repair and retrofit. The algorithm is tested with simulated and measured El Centro earthquake response data from a four storey non-linear steel frame structure and simulated data from a two storey non-linear hybrid rocking structure. The steel frame and rocking structures exhibit contrasting dynamic response and are thus used to highlight the impact of baseline model selection in SHM. In simulation, the algorithm identifies stiffness to within 3.5% with 90% confidence, and permanent displacement to within 7.5% with 90% confidence. Using measured data for the frame structure, the algorithm identifies final residual deformation to within 1.5% and identifies realistic stiffness values in comparison to values predicted from pushover analysis. For the rocking structure, the algorithm accurately identifies the different regimes of motion and linear stiffness comparable to estimates from previous research. Overall, the method is seen to be accurate, effective and realtime capable, with the non-linear baseline model more accurately identifying damage in both of the disparate structures examined.
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Silva, Muñoz Rodrigo. "Structural Health Monitoring Using Embedded Fiber Optic Strain Sensors." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/SilvaMunozR2008.pdf.

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Shinde, Abhijeet Dipak. "A wavelet packet based sifting process and its application for structural health monitoring." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0824104-222824/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Wooden Structure; Damage Detection; Structural Health Monitoring; Instantaneous Modal Parameters; Wavelet Analysis; Time Varying Systems; Sifting Process. Includes bibliographical references (p. 77-82).
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Hera, Adriana. "Instantaneous modal parameters and their applications to structural health monitoring." Link to electronic dissertation, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-121905-163738/.

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Dissertation (Ph.D..) -- Worcester Polytechnic Institute.
Keywords: structural health monitoring; wavelet transform; time varying vibration modes; instantaneous modal parameters. Includes bibliographical references (p.181-186).
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Lu, Yinghui. "Analysis and modeling of diffuse ultrasonic signals for structural health monitoring." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-07052007-225427/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.
Durgin, Gregory, Committee Member ; Vachtsevanos, George, Committee Member ; Michaels, Thomas, Committee Member ; Michaels, Jennifer, Committee Chair ; Jacobs, Laurence, Committee Member.
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Akin, Tugba. "Structural Monitoring And Analysis Of Steel Truss Railroad Bridges." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614825/index.pdf.

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Railroad bridges are the most important connection parts of railroad networks. These bridges are exposed to heavier train loads compared to highway bridges as well as various detrimental ambient conditions during their life span. The railroad bridges in Turkey are mostly constructed during the late Ottoman and first periods of the Turkish Republic
therefore, they are generally close to about 100 years of age
their inspection and maintenance works are essential. Structural health monitoring (SHM) techniques are widely used around the world in order to increase the effectiveness of the inspection and maintenance works and also evaluate structural reliability. Application of SHM methods on railway bridges by static and dynamic measurements over short and long durations give important structural information about bridge members&rsquo
load level and overall bridge structure in terms of vibration frequencies, deflections, etc. Structural Reliability analysis provides further information about the safety of a structural system and becomes even more efficient when combined with the SHM studies. In this study, computer modeling and SHM techniques are used for identifying structural condition of a steel truss railroad bridge in Usak, Turkey, which is composed of six spans with 30 m length each. The first two spans of the bridge were rebuilt about 50 years ago, which had construction plans and are selected as pilot case for SHM and evaluation studies in this thesis. Natural frequencies are obtained by using 4 accelerometers and a dynamic data acquisition system (DAS). Furthermore, mid span vertical deflection member strains and bridge accelerations are obtained using a DAS permanently left on site and then compared with the computer model analyses results. SHM system is programmed for triggering by the rail load sensors developed at METU and an LVDT to collect mid span deflection high speed data from all sensors during train passage. The DAS is also programmed to collect slow speed data (once at every 15 minutes) for determination of average ambient conditions such as temperature and humidity and all bridge sensors during long term monitoring. Structural capacity and reliability indices for stress levels of bridge members are determined for the measured and simulated train loads to determine structural condition of bridge members and connections. Earthquake analyses and design checks for bridge members are also conducted within the scope of this study.
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Ojeda, Alejandro P. "MATLAB implementation of an operational modal analysis technique for vibration-based structural health monitoring." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/74412.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 72-73).
Vibration-based structural health monitoring (SHM) has become an attractive solution for the global monitoring and evaluation of damage in structures. Numerous damage detection schemes used in vibration-based SHM require knowledge of the modal properties of the structure under evaluation in its current state. The technique of operational modal analysis allows for these modal properties to be obtained by using the structure's dynamic response to ambient excitation. Using MATLAB, a type of operational modal analysis technique called time domain decomposition (TDD) based on [15] was implemented. The MATLAB TDD implementation was applied to the dynamic responses from two finite element models of simply-supported beams and their modal frequencies and shapes were extracted. The first three modal frequencies were obtained with less than 6 percent error from the actual values and the fundamental mode shape values obtained contained negligible deviations from the actual mode shape values. However, the higher order mode shapes obtained were more inaccurate, suggesting limitations to the current MATLAB TDD implementation. Lastly, changes to the moment of inertia of the simply-supported beam models were used to simulate damage in the finite element models and cause their fundamental mode frequency to change. The MATLAB TDD implementation was able to distinguish changes in the fundamental frequency of both finite element models with a resolution of approximately 1.7 radians per second (7.2 percent).
by Alejandro P. Ojeda.
M.Eng.
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Essegbey, John W. "Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342729241.

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Terrell, Thomas. "Structural health monitoring for damage detection using wired and wireless sensor clusters." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5055.

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Sensing and analysis of a structure for the purpose of detecting, tracking, and evaluating damage and deterioration, during both regular operation and extreme events, is referred to as Structural Health Monitoring (SHM). SHM is a multi-disciplinary field, with a complete system incorporating sensing technology, hardware, signal processing, networking, data analysis, and management for interpretation and decision making. However, many of these processes and subsequent integration into a practical SHM framework are in need of development. In this study, various components of an SHM system will be investigated. A particular focus is paid to the investigation of a previously developed damage detection methodology for global condition assessment of a laboratory structure with a decking system. First, a review of some of the current SHM applications, which relate to a current UCF Structures SHM study monitoring a full-scale movable bridge, will be presented in conjunction with a summary of the critical components for that project. Studies for structural condition assessment of a 4-span bridge-type steel structure using the SHM data collected from laboratory based experiments will then be presented. For this purpose, a time series analysis method using ARX models (Auto-Regressive models with eXogeneous input) for damage detection with free response vibration data will be expanded upon using both wired and wireless acceleration data. Analysis using wireless accelerometers will implement a sensor roaming technique to maintain a dense sensor field, yet require fewer sensors. Using both data types, this ARX based time series analysis method was shown to be effective for damage detection and localization for this relatively complex laboratory structure. Finally, application of the proposed methodologies on a real-life structure will be discussed, along with conclusions and recommendations for future work.
ID: 029810361; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.C.E.)--University of Central Florida, 2011.; Includes bibliographical references (p. 102-114).
M.S.C.E.
Masters
Civil, Environmental and Construction Engineering
Engineering and Computer Science
Civil Engineering
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Books on the topic "Structural analysis (Engineering) Structural health monitoring"

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International Workshop on Structural Health Monitoring (2nd 1999 Stanford, Calif.). Structural health monitoring, 2000. Lancaster, PA: Technomic Pub. Co., 1999.

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Glišić, Branko. Fibre optic methods for structural health monitoring. Chichester, England: John Wiley & Sons, 2007.

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Gopalakrishnan, Srinivasan. Computational Techniques for Structural Health Monitoring. London: Springer-Verlag London Limited, 2011.

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Ostachowicz, Wieslaw. New Trends in Structural Health Monitoring. Vienna: Springer Vienna, 2013.

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Rodellar, José, Diego Alexander Tibaduiza Burgos, and Luis Eduardo Mujica. Emerging design solutions in structural health monitoring systems. Hershey, PA: Engineering Science Reference, 2015.

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Pawar, Prashant M. Structural health monitoring using genetic fuzzy systems. London: Springer, 2011.

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Wenzel, Helmut. Health monitoring of bridges. Hoboken: Wiley, 2009.

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Vic.) Asia-Pacific Workshop on Structural Health Monitoring (4th 2012 Melbourne. Structural health monitoring: Research and applications : peer reviewed papers from the 4th Asia-Pacific Workshop on Structural Health Monitoring, December 5-7, 2012, Melbourne, Australia. Durnten-Zurich, Switzerland: TTP, Trans Tech Publications Ltd, 2013.

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Structural sensing, health monitoring, and performance evaluation. Boca Raton: Taylor & Francis, 2010.

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Health monitoring of structural materials and components: Methods with applications. Chichester, UK: John Wiley & Sons, 2007.

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Book chapters on the topic "Structural analysis (Engineering) Structural health monitoring"

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Hızal, Çağlayan, and Engin Aktas¸. "Structural Health Monitoring-Integrated Reliability Assessment of Engineering Structures." In Reliability-Based Analysis and Design of Structures and Infrastructure, 117–28. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003194613-9.

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Ferraris, M., M. Civera, R. Ceravolo, C. Surace, and R. Betti. "Using Enhanced Cepstral Analysis for Structural Health Monitoring." In Lecture Notes in Mechanical Engineering, 150–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8331-1_11.

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Cowled, Craig J. L., David P. Thambiratnam, Tommy H. T. Chan, and Andy C. C. Tan. "Structural Complexity in Structural Health Monitoring: Preliminary Experimental Modal Testing and Analysis." In Lecture Notes in Mechanical Engineering, 183–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06966-1_18.

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Dimino, I., and V. Quaranta. "A Wavelet-Based System for Structural Health Monitoring of Aeronautic Structures." In Experimental Analysis of Nano and Engineering Materials and Structures, 453–54. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6239-1_225.

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O’Brien, Eddie. "Biomimetic Guided Structural Health Monitoring for Civil Aircraft." In Experimental Analysis of Nano and Engineering Materials and Structures, 409–10. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6239-1_203.

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Dattaguru, B. "Structural Health Monitoring: Nonlinear Effects in the Prognostic Analysis of Crack Growth in Structural Joints." In Springer Tracts in Mechanical Engineering, 375–83. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1913-2_22.

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Şafak, Erdal, Eser Çaktı, and Yavuz Kaya. "Recent Developments on Structural Health Monitoring and Data Analyses." In Geotechnical, Geological, and Earthquake Engineering, 331–55. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9544-2_14.

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Scuro, Carmelo, Renato Sante Olivito, Francesco Lamonaca, and Domenico Luca Carnì. "Structural Health Monitoring Based on Artificial Intelligence Algorithm and Acoustic Emission Analysis." In Lecture Notes in Civil Engineering, 258–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64908-1_24.

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Scuro, Carmelo, Renato Sante Olivito, Francesco Lamonaca, and Domenico Luca Carnì. "Structural Health Monitoring Based on Artificial Intelligence Algorithm and Acoustic Emission Analysis." In Lecture Notes in Civil Engineering, 258–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64908-1_24.

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Masciotta, Maria-Giovanna, Alberto Barontini, Luís F. Ramos, Paulo Amado-Mendes, and Paulo B. Lourenço. "A Bio-inspired Framework for Highly Efficient Structural Health Monitoring and Vibration Analysis." In Lecture Notes in Civil Engineering, 455–68. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67443-8_39.

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Conference papers on the topic "Structural analysis (Engineering) Structural health monitoring"

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"Research on Fatigue Life of Lifting Equipment Based on Nonlinear Cumulative Damage Theory." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-43.

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Abstract. The lifting equipment is applied more and more extensively with the fast development of economy. The fatigue and safety problems of lifting equipment in service are more and more prominent. This paper presents a nonlinear fatigue damage and life assessment approach for existing lifting equipment and the nonlinear effects of the fatigue damage accumulation due to random dynamic stress spectrum. The stress spectrum monitoring data was analyzed by the modified four peak-valley values fast rain-flow counting method for fatigue analysis. By considering nonlinear effects, the calculation for cumulative damage and prediction for fatigue life of the lifting equipment's hot region were calculated based on the nonlinear damage theory and the nominal stress method. The predicted fatigue damages are different when using the linear and nonlinear fatigue damage rules. According to the engineering application results, the real crack generation time is consistent with that of our estimation method, which demonstrates that our nonlinear prediction model and method for fatigue life are effective. The nonlinear damage theory is recommended for use in fatigue and damage prediction of lifting equipment in service.
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Savin, Adriana, Rozina Steigmann, and Gabriel-Silviu Dobrescu. "Metamaterial Sensors for Structural Health Monitoring." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20596.

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Monitoring large structures is gaining a lot of interest, diagnosis and prognosis methods, as well as sensors based on different physical principles being developed. The monitoring is in tight connection with the nondestructive examination. This paper presents a new type of passive wireless sensors for monitoring of strain-stress having a special type of metamaterial as sensitive element, namely a split ring resonator. In order to increase the sensitivity and the tuning on a desired interrogation frequency, self-assembled layer-by-layer single wall carbon nanotubes/graphene method is used. In the same time, radio-frequency identification (RFID) tag, an essential element for wireless sensors, can be realized using split ring resonators.
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Ballinger, Robert S., and David W. Herrin. "Structural Health Monitoring Using Modal Strain Energy." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0151.

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Abstract This research combines analytical and experimental modal analysis techniques to verify the structural integrity or monitor the “health” of a dynamic structure. Central to the procedure is the development of a baseline dynamic fingerprint model of the structure. The dynamic fingerprint is verified with experimental modal analysis and correlation. After the structure is placed into service, damage can be determined by comparing the current dynamic response with the baseline dynamic fingerprint response. The unique aspect of this procedure is that the current dynamic response is enforced on the undamaged baseline dynamic fingerprint model. Should damage exist, the structure is forced to deform in an unnatural manner, and high strain energy results. Significant differences in the normalized modal or operating strain energy density identify structural regions where a loss of stiffness, weakening of the structure, and/or damage has occurred. This identification of a potentially “unhealthy” structural region allows a quick visual inspection of the region or further analytical and/or experimental submodelling of the area to precisely identify the damage. The method is ideally suited to CAE application. The method is demonstrated analytically and experimentally for two structures: an eight-bay cantilevered truss structure and a rectangular plate with various boundary conditions.
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Hasan, Zeaid, Fares Hasweh, Omar Abu Al-Nadi, and Ghassan Atmeh. "An Automated Structural Health Monitoring System." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-64778.

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Structural health monitoring (SHM) is the process of implementing a damage identification strategy which can be utilized in several applications including aerospace, civil and mechanical engineering infrastructure. Damage is defined as changes to the material and/or geometric properties of these systems. These changes adversely affect the current or future performance of the system. In order to identify damage in a suitable and meaningful manner, the damaged state is compared with other usually undamaged states. This study focuses on a structural health monitoring (SHM) system based on detecting shifts in natural frequencies of the structure. This structural health monitoring system incorporates a low power wireless transmitter that sends a warning signal when damage is detected in a structure. The damage detection technique is implemented on composite structures which are widely used in many applications including aeronautical and aerospace. An automated damage detection system capable of providing information of damage locations based on the finite element analysis and able to compare damage events to other historical data is also proposed in this paper and initially implemented using a microcontroller chip. Moreover, a control methodology using piezoelectric fiber composites, such as active fiber composites (AFCs) and microfiber composites (MFCs), is included as part of the system for vibration suppression purposes. The advantages of using piezoelectric fiber composite actuators are their high performance, flexibility, and durability when compared with the traditional piezoceramic (PZT) actuators. The proposed system may be implemented in many structural components such as aircraft frames and bridges. This SHM technology may help replace the current time-based maintenance scheme with a condition-based one. The condition-based maintenance scheme relies on the ability to monitor the condition of the system and supply information of damage detection to allow a corrective action to be taken.
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Yazawa, Toru. "Everyday Life Quantification Using mDFA: Heart Health Monitoring and Structural Health Monitoring." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-48018.

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The aim of this study was to make a method usable in an early detection of malfunction, e.g., abnormal vibration/fluctuation in recorded signals. We conducted experimentations of heart health and structural health monitoring. We collected natural world signals, e.g., heartbeat fluctuation and mechanical vibration. For the analysis, we used modified detrended fluctuation analysis (mDFA) method that we have made recently. mDFA calculated the scaling exponent (SI, the acronym SI is derived from the scaling indices) from the time series data, e.g., R-R interval time series obtained from electrocardiograms. In the present study, peaks were identified by our own method. In every single mDFA computation, we identified ∼2000 consecutive peaks from a data: “2000” was necessary number to conduct mDFA. mDFA was able to distinguish between normal and abnormal behaviors: Normal healthy hearts exhibited an SI around 1.0, which is a phenomena comparable to 1/f fluctuation. Job-related stressful hearts and extrasystolic hearts both exhibited a low SI such as 0.7. Normally running car’s vibration — recorded steering wheel vibration — exhibited an SI around 0.5, which is white noise like fluctuation. Normally spinning ball-bearings (BB) exhibited an SI around 0.1, which belongs to the anti-correlation phenomena. A malfunctioning BB showed an increased SI. At an SI value over 0.2, an inspector must check BB’s correct functioning. Here we propose that healthiness in various cyclic vibration behaviors can be quantitatively analyzed by mDFA.
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Mani, Girindra, D. Dane Quinn, and Mary E. F. Kasarda. "Structural Health Monitoring of Rotordynamic Systems by Wavelet Analysis." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15930.

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This work describes a foundation of sophisticated diagnostic techniques for the detection of shaft cracks in rotordynamic systems, considering the dynamical behavior of a rotating cracked shaft under the application of external loads. The response is modeled as a modified Jeffcott rotor, while the crack is assumed to induce a time-varying stiffness in the model. The focus of this work is the development of external loading strategies to create damage sensitive measures of vibration response and then analyze that using advanced technologies such as wavelet analysis. This will enable the detection of the crack depth, as represented by the magnitude of the damage-induced time-varying stiffness, from vibration measurements. This entails developing external forcing functions for which features of the vibration response are sensitive to the presence of the damage. The development of such inputs is based on a multiple-scales analysis of the full equations of motion, including the time-varying stiffness. From this, a resonance (called combination resonance) is identified between the operating speed of the shaft, the fundamental frequency of the shaft, and the frequency of the external forcing. When the system is operated at this resonant condition, the translational vibrations of the shaft contain a spectral component near the fundamental shaft frequency that is proportional to the amplitude of the time-varying stiffness. The resonance bandwidth, obtained from this analysis, enables us to build a framework for the development of damage detection techniques for rotating machinery. Continuous Wavelet Transform (CWT) is applied to the vibration response of a rotordynamic system that utilizes harmonic forcing satisfying combination resonance. The variation of wavelet coefficients with respect to the variation of different system parameters is examined. Attention is focused on how the resonant bandwidth affects the variation of wavelet coefficients as crack grows.
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Shih, Hui-Ru, and Albert C. McIntyre. "Structural Health Monitoring Using Piezoelectric Transducers and Wavelets." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62212.

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Structural Health Monitoring (SHM) is the process of monitoring and assessing the state of health for aerospace, civil, and mechanical engineering infrastructure. SHM offers the opportunity to reduce inspection efforts and optimize maintenance and mission planning. SHM is a highly emerging field of technology. It brings together a variety of disciplines. To stimulate students’ desire for pursuing advanced study in science, technology, engineering, and mathematics (STEM) and well prepare them for their future careers, STEM educators need to dedicate their efforts to educate the students with this emerging technology. SHM is a field that requires a significant amount of background knowledge to build upon. At Jackson State University (JSU), four course modules (Smart Materials, Data Acquisition Systems, Lamb Waves, and Wavelet Analysis) have been developed and integrated into an existing course to help undergraduate students gain practical experience and have a firm grasp on this emerging vital tool. After taking the class, several students were selected to participate in a research activity sponsored by the Center for Undergraduate Research (CUR) at JSU. This paper describes the content covered in the modules as well as summarizes student perceptions of their learning and research experiences.
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Díaz Valdés, Sergio H., and Costas Soutis. "A Structural Health Monitoring System for Laminated Composites." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21539.

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Abstract This investigation examines the application of low frequency Lamb waves for the detection of delaminations in thick composite laminates. Surface mounted piezoelectric devices were excited with a tone burst of few cycles generating a stress wave that propagates along the structure. Experiments were carried out on composite beam specimens where wave propagation distances over 2 m were achieved and artificially induced delaminations as small as 1 cm2 were successfully identified. The resonance spectrum method, which is based on the study of spectra obtained by forced mechanical resonance of samples using sine-sweep excitation, was used for measuring the A0 Lamb mode phase velocity. Finite element analyses of wave generation and propagation in wide laminated plates are also presented. The feasibility of employing piezoelectric devices for the development of smart structures, where a small and lightweight transducer system design is required, has been demonstrated.
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Littlewood, David J., Kyran Mish, and Kendall Pierson. "Peridynamic Simulation of Damage Evolution for Structural Health Monitoring." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86400.

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Modal-based methods for structural health monitoring require the identification of characteristic frequencies associated with a structure’s primary modes of failure. A major difficulty is the extraction of damage-related frequency shifts from the large set of often benign frequency shifts observed experimentally. In this study, we apply peridynamics in combination with modal analysis for the prediction of characteristic frequency shifts throughout the damage evolution process. Peridynamics, a nonlocal extension of continuum mechanics, is unique in its ability to capture progressive material damage. The application of modal analysis to peridynamic models enables the tracking of structural modes and characteristic frequencies over the course of a simulation. Shifts in characteristic frequencies resulting from evolving structural damage can then be isolated and utilized in the analysis of frequency responses observed experimentally. We present a methodology for quasi-static peridynamic analyses, including the solution of the eigenvalue problem for identification of structural modes. Repeated solution of the eigenvalue problem over the course of a transient simulation yields a data set from which critical shifts in modal frequencies can be isolated. The application of peridynamics to modal analysis is demonstrated on the benchmark problem of a simply-supported beam. The computed natural frequencies of an undamaged beam are found to agree well with the classical local solution. Analyses in the presence of cracks of various lengths are shown to reveal frequency shifts associated with structural damage.
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Mou, J. Q., L. Martua, Y. Q. Yu, Z. M. He, C. L. Du, J. L. Zhang, and E. H. Ong. "Structural Health Monitoring Using PZT Transducer Network and Lamb Waves." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37653.

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Structure health monitoring (SHM) using ultrasonic waves is an emerging technology that can be applied to real-time detect, locate and quantify the structural damages in aircraft structures. In this paper, the monitoring of crack growth at rivet holes in an aluminum test plate using a PZT transducer network and Lamb waves is investigated. The thin disc PZT transducers surface mounted at the test plate are used as actuators to transmit the windowed sinewave bursts and sensors to receive the ultrasonic Lamb waves. The symmetrical S0 mode and antisymmetrical A0 mode of the Lamb waves in the structures are studied with correlated theoretical, experimental and numerical analysis. The optimal excitation frequency is determined for the test plate. Finite element method (FEM) numerical models for simulations of the wave propagations and interactions with the holes and cracks in the plate are developed and verified with the experimental results. The wave responses modes and characteristics for detection of the cracks at the rivet holes are analyzed. The Lamb wave signals in the PZT transducer network are processed with the short time Fourier transform (STFT). It is demonstrated that the time of flight and the energy transmission ratio of the S0 wave are sensitive to the cracks in the structure.
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Reports on the topic "Structural analysis (Engineering) Structural health monitoring"

1

Everhart-Erickson, Michael Charles. Video-Based Dynamic Measurement & Analysis for Structural Health Monitoring. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1435550.

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Grandhi, Ramana V., and Randy Tobe. Design and Analysis of Advanced Materials in a Thermal/Acoustic Environment. Delivery Order 0007: Volume 1 - Structural Health Monitoring. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada517384.

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