Nichols [1] explored the role of ambient excitation and empirical modeling in detecting damage in an offshore structure excited with the output of a stochastic process conforming to the ambient Pierson–Moskowitz wave distribution.
Ghoshal et al [2] explored four different algorithms, like transmittance function, resonant comparison, operational deflection shape, and wave propagation methods for detecting damage by moisture absorption, fatigue, wind gusts or lightening strikes on wind turbine blades through the measurement of the vibration response when it is excited using piezoceramic actuator bonded to the blade and sensed through either piezoceramic sensor patches bonded to the blade, or a scanning laser doppler vibrometer.
Liberatore et al [3] developed a model based fault detection filter which is based on a left eigenstructure assignment approach to accommodate system sensitivities that are revealed as ill-conditioned matrices formed from the eigenvectors in the construction of the detection filter gains.
The impact damages were detected by Takeda et al [4] using the spectrum change of the wavelength shift of the FBG sensor output for the durability tests of a composite wing structure and then validated by using NDI (non-destructive inspection) technologies: AE (acoustic emission) sensors, an ultrasonic C-scan, and pulsed heating thermography.
Ko and Ni [5] explored long term structural health monitoring for large-scale bridge, in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse.
Fritzen and Kraemer [6] discussed the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain for SHM of civil and aerospace engineering structures as well as off-shore wind energy plants, where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time.
Taha and Lucero [7] introduced pattern recognition and damage detection methods with fuzzy sets; where Bayesian updating was used to demarcate levels of damage into fuzzy sets accommodating the uncertainty associated with the ambiguous damage states.
To evaluate structural condition, Catbas et al [8] used multi-input–multi-output dynamic data to obtain modal flexibility and then the curvature is calculated from the deflected shapes using this modal flexibility as opposed to using modal vectors.
Kawchuk et al [9] employed structural health monitoring techniques to identify the presence, location and magnitude of structural alterations within the spine using frequency response functions and artificial neural network.
Silva et al [10] investigated structural health monitoring using vibration data in a three-stage process: reduction of the time-series data using principle component analysis, the development of a data-based model using an auto-regressive moving average model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach, like fuzzy c-means, Gustafson–Kessel algorithms.
Achenbach [11] have discussed about the health monitoring of safety-critical structures such as aircraft, bridges, nuclear reactors and dams, which cannot be allowed to fail; using the techniques of non-destructive inspection to provide continuous or on-demand information about the state of a structure, so that an assessment of the structural integrity can be made at any time, and timely remedial actions may be taken as necessary through a large number of sensors.
Teo et al [12] demonstrated the optimum combinations of frequency and location of the sensor in monitoring sub-surface defects using scattering of stress waves on aircraft structures with multi-layered construction and geometry variation.
Li and Chan [13] developed a structural health assessment methodology using data acquired from structural health monitoring system installed on long-span bridges considering a fatigue crack growth criterion based on the concept of the continuum damage mechanics for pre-determining the global state of the bridge.
Mayer et al [14] examined model based monitoring of structures using piezoelectric actuators.
1. Structural health monitoring of offshore structures using ambient excitation Applied Ocean Research, Volume 25, Issue 3, June 2003, Pages 101-114 J. M. Nichols
2. Structural health monitoring techniques for wind turbine blades Journal of Wind Engineering and Industrial Aerodynamics, Volume 85, Issue 3, 24 April 2000, Pages 309-324 Anindya Ghoshal, Mannur J. Sundaresan, Mark J. Schulz, P. Frank Pai
3. Application of a fault detection filter to structural health monitoring Automatica, Volume 42, Issue 7, July 2006, Pages 1199-1209 Sauro Liberatore, Jason L. Speyer, Andy Chunliang Hsu
4. Structural health monitoring of composite wing structure during durability test Composite Structures, Volume 79, Issue 1, June 2007, Pages 133-139 S. Takeda, Y. Aoki, T. Ishikawa, N. Takeda, H. Kikukawa
5. Technology developments in structural health monitoring of large-scale bridges Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1715-1725 J.M. Ko, Y.Q. Ni
6. Self-diagnosis of smart structures based on dynamical properties Mechanical Systems and Signal Processing, Volume 23, Issue 6, August 2009, Pages 1830-1845 C.-P. Fritzen, P. Kraemer
7. Damage identification for structural health monitoring using fuzzy pattern recognition Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1774-1783 M.M. Reda Taha, J. Lucero
8. Conceptual damage-sensitive features for structural health monitoring: Laboratory and field demonstrations Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1650-1669 F. Necati Catbas, Mustafa Gul, Jason L. Burkett
9. Structural health monitoring to detect the presence, location and magnitude of structural damage in cadaveric porcine spinesJournal of Biomechanics, Volume 42, Issue 2, 19 January 2009, Pages 109-115 Gregory Neil Kawchuk, Colleen Decker, Ryan Dolan, Jason Carey
10. Structural damage detection by fuzzy clustering Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1636-1649 Samuel da Silva, Milton Dias Júnior, Vicente Lopes Junior, Michael J. Brennan
11. Structural health monitoring – What is the prescription? Mechanics Research Communications, Volume 36, Issue 2, March 2009, Pages 137-142 Jan D. Achenbach
12. Optimal placement of sensors for sub-surface fatigue crack monitoring Theoretical and Applied Fracture Mechanics, In Press, Corrected Proof, Available online 14 July 2009 Y.H. Teo, W.K. Chiu, F.K. Chang, N. Rajic
13. Fatigue criteria for integrity assessment of long-span steel bridge with health monitoring Theoretical and Applied Fracture Mechanics, Volume 46, Issue 2, October 2006, Pages 114-127 Z.X. Li, T.H.T. Chan
14. An approach for the model based monitoring of piezoelectric actuators Computers & Structures, Volume 86, Issues 3-5, February 2008, Pages 314-321 Dirk Mayer, Heiko Atzrodt, Sven Herold, Martin Thomaier
Ghoshal et al [2] explored four different algorithms, like transmittance function, resonant comparison, operational deflection shape, and wave propagation methods for detecting damage by moisture absorption, fatigue, wind gusts or lightening strikes on wind turbine blades through the measurement of the vibration response when it is excited using piezoceramic actuator bonded to the blade and sensed through either piezoceramic sensor patches bonded to the blade, or a scanning laser doppler vibrometer.
Liberatore et al [3] developed a model based fault detection filter which is based on a left eigenstructure assignment approach to accommodate system sensitivities that are revealed as ill-conditioned matrices formed from the eigenvectors in the construction of the detection filter gains.
The impact damages were detected by Takeda et al [4] using the spectrum change of the wavelength shift of the FBG sensor output for the durability tests of a composite wing structure and then validated by using NDI (non-destructive inspection) technologies: AE (acoustic emission) sensors, an ultrasonic C-scan, and pulsed heating thermography.
Ko and Ni [5] explored long term structural health monitoring for large-scale bridge, in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse.
Fritzen and Kraemer [6] discussed the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain for SHM of civil and aerospace engineering structures as well as off-shore wind energy plants, where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time.
Taha and Lucero [7] introduced pattern recognition and damage detection methods with fuzzy sets; where Bayesian updating was used to demarcate levels of damage into fuzzy sets accommodating the uncertainty associated with the ambiguous damage states.
To evaluate structural condition, Catbas et al [8] used multi-input–multi-output dynamic data to obtain modal flexibility and then the curvature is calculated from the deflected shapes using this modal flexibility as opposed to using modal vectors.
Kawchuk et al [9] employed structural health monitoring techniques to identify the presence, location and magnitude of structural alterations within the spine using frequency response functions and artificial neural network.
Silva et al [10] investigated structural health monitoring using vibration data in a three-stage process: reduction of the time-series data using principle component analysis, the development of a data-based model using an auto-regressive moving average model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach, like fuzzy c-means, Gustafson–Kessel algorithms.
Achenbach [11] have discussed about the health monitoring of safety-critical structures such as aircraft, bridges, nuclear reactors and dams, which cannot be allowed to fail; using the techniques of non-destructive inspection to provide continuous or on-demand information about the state of a structure, so that an assessment of the structural integrity can be made at any time, and timely remedial actions may be taken as necessary through a large number of sensors.
Teo et al [12] demonstrated the optimum combinations of frequency and location of the sensor in monitoring sub-surface defects using scattering of stress waves on aircraft structures with multi-layered construction and geometry variation.
Li and Chan [13] developed a structural health assessment methodology using data acquired from structural health monitoring system installed on long-span bridges considering a fatigue crack growth criterion based on the concept of the continuum damage mechanics for pre-determining the global state of the bridge.
Mayer et al [14] examined model based monitoring of structures using piezoelectric actuators.
1. Structural health monitoring of offshore structures using ambient excitation Applied Ocean Research, Volume 25, Issue 3, June 2003, Pages 101-114 J. M. Nichols
2. Structural health monitoring techniques for wind turbine blades Journal of Wind Engineering and Industrial Aerodynamics, Volume 85, Issue 3, 24 April 2000, Pages 309-324 Anindya Ghoshal, Mannur J. Sundaresan, Mark J. Schulz, P. Frank Pai
3. Application of a fault detection filter to structural health monitoring Automatica, Volume 42, Issue 7, July 2006, Pages 1199-1209 Sauro Liberatore, Jason L. Speyer, Andy Chunliang Hsu
4. Structural health monitoring of composite wing structure during durability test Composite Structures, Volume 79, Issue 1, June 2007, Pages 133-139 S. Takeda, Y. Aoki, T. Ishikawa, N. Takeda, H. Kikukawa
5. Technology developments in structural health monitoring of large-scale bridges Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1715-1725 J.M. Ko, Y.Q. Ni
6. Self-diagnosis of smart structures based on dynamical properties Mechanical Systems and Signal Processing, Volume 23, Issue 6, August 2009, Pages 1830-1845 C.-P. Fritzen, P. Kraemer
7. Damage identification for structural health monitoring using fuzzy pattern recognition Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1774-1783 M.M. Reda Taha, J. Lucero
8. Conceptual damage-sensitive features for structural health monitoring: Laboratory and field demonstrations Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1650-1669 F. Necati Catbas, Mustafa Gul, Jason L. Burkett
9. Structural health monitoring to detect the presence, location and magnitude of structural damage in cadaveric porcine spinesJournal of Biomechanics, Volume 42, Issue 2, 19 January 2009, Pages 109-115 Gregory Neil Kawchuk, Colleen Decker, Ryan Dolan, Jason Carey
10. Structural damage detection by fuzzy clustering Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1636-1649 Samuel da Silva, Milton Dias Júnior, Vicente Lopes Junior, Michael J. Brennan
11. Structural health monitoring – What is the prescription? Mechanics Research Communications, Volume 36, Issue 2, March 2009, Pages 137-142 Jan D. Achenbach
12. Optimal placement of sensors for sub-surface fatigue crack monitoring Theoretical and Applied Fracture Mechanics, In Press, Corrected Proof, Available online 14 July 2009 Y.H. Teo, W.K. Chiu, F.K. Chang, N. Rajic
13. Fatigue criteria for integrity assessment of long-span steel bridge with health monitoring Theoretical and Applied Fracture Mechanics, Volume 46, Issue 2, October 2006, Pages 114-127 Z.X. Li, T.H.T. Chan
14. An approach for the model based monitoring of piezoelectric actuators Computers & Structures, Volume 86, Issues 3-5, February 2008, Pages 314-321 Dirk Mayer, Heiko Atzrodt, Sven Herold, Martin Thomaier
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