Johnson et al [1] presented work on smart sensor arrays for distributed structural health monitoring and damage diagnosis. The goal of their work was to implement local vibration-based diagnostic algorithms inside a smart ‘black box’ to demonstrate the feasibility of distributed health monitoring for damage detection and location. Dynamic transmissibility features for SHM and the smart-processing platform have been described in detail and various damage configurations in two large test structures, a representative three-storey building and a rotorcraft fuselage, have been diagnosed. Their results showed that the near real-time integrated monitoring system works well in spite of certain limited environmental fluctuations (e.g. temperature, input levels) and boundary condition non-linearity. Wired piezoelectric arrays of accelerometers are implemented in conjunction with the black box.
To deal with the diverse, heterogeneous and distributed information obtained by density or different kinds of sensors at different sites on a large scale engineering structure, multi-agent system (MAS) based structural health monitoring (SHM) technology have been adopted by Zhao et al [2] with a general framework considering function design, ontology design, individual agent design, facilitator design, society behavior and learning behavior design which can be plugged in any multi-agent based SHM system and tested with a case study.
Zhao et al [3] introduced an evaluation on a multi-agent system based structural health monitoring to validate the efficiency of the multi-agent technology, which can automatically choose sensing object, can self-organize the sensor network and discard useless sensor data, and can recognize three typical kinds of structure states which may indicate structural damages including impact load, joint failure, strain distribution change on every substructure and the edge area between two adjacent substructures.
Wu et al [4] presented a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring including the wireless strain gauge node, wireless PZT node and wireless USB station which can automatically allocate SHM tasks; self-organize the sensor network and aggregate different sensor information using six different SHM agents.
Son et al [5] considered support vector machine, linear discriminate analysis, k-nearest neighbors, and random forests algorithm as classifiers for fault diagnosis of machine using three types of signals involving vibration, current, and flux from induction motors.
Wang et al [6] identified a through-thickness hole in a stiffened composite panel using guided wave-based damage diagnostic algorithm, which is based on the probability of the presence of damage in the monitoring area estimated using correlation coefficients of Lamb wave signals from an active sensor network.
Shah and Ribakov [7] presented findings on nonlinear ultrasonic testing of concrete with different frequency transducers. As concrete is a high attenuation material, the change in fundamental amplitude or attenuation was found sensitive to using transducers with different frequency. The test results were also analyzed to find correlation between w/c, wave attenuation and higher harmonic generation based nonlinear parameters.
Overbey and Todd [8] evaluated input and output noise sensitivity on transfer entropy damage index as a feature for nonlinearity detection and linear damage identification, which requires the estimation of non-parametric one-, two-, and three-dimensional probability density functions.
Gul and Catbas [9] investigated statistical pattern recognition methods in the context of SHM using time series modeling, i.e. auto-regressive models, in conjunction with Mahalanobis distance-based outlier detection algorithms to identify different types of structural changes on different test structures.
Lopez and Klijn [10] used distance similarity matrix of dimensionally reduced data and shown ensembles perform better than any single-feature extraction method.
1. Distributed structural health monitoring with a smart sensor arrayMechanical Systems and Signal Processing, Volume 18, Issue 3, May 2004, Pages 555-572Timothy J. Johnson, Rebecca L. Brown, Douglas E. Adams, Mark Schiefer
2. Designing strategy for multi-agent system based large structural health monitoringExpert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 1154-1168Xia Zhao, Shenfang Yuan, Zhenhua Yu, Weisong Ye, Jun Cao
3. An evaluation on the multi-agent system based structural health monitoring for large scale structures Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 4900-4914 Xia Zhao, Shenfang Yuan, Hengbao Zhou, Hongbing Sun, Lei Qiu
4. Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring Expert Systems with Applications, In Press, Corrected Proof, Available online 7 July 2009 Jian Wu, Shenfang Yuan, Sai Ji, Genyuan Zhou, Yang Wang, Zilong Wang
5. Development of smart sensors system for machine fault diagnosisExpert Systems with Applications, Volume 36, Issue 9, November 2009, Pages 11981-11991Jong-Duk Son, Gang Niu, Bo-Suk Yang, Don-Ha Hwang, Dong-Sik Kang
6. Probability of the presence of damage estimated from an active sensor network in a composite panel of multiple stiffenersComposites Science and Technology, Volume 69, Issue 13, October 2009, Pages 2054-2063Dong Wang, Lin Ye, Ye Lu, Zhongqing Su
7. Non-destructive evaluation of concrete in damaged and undamaged statesMaterials & Design, Volume 30, Issue 9, October 2009, Pages 3504-3511A.A. Shah, Y. Ribakov
8. Effects of noise on transfer entropy estimation for damage detectionMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2178-2191L.A. Overbey, M.D. Todd
9. Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verificationsMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2192-2204Mustafa Gul, F. Necati Catbas
10. Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studiesMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2287-2300Israel Lopez, Nesrin Sarigul-Klijn
To deal with the diverse, heterogeneous and distributed information obtained by density or different kinds of sensors at different sites on a large scale engineering structure, multi-agent system (MAS) based structural health monitoring (SHM) technology have been adopted by Zhao et al [2] with a general framework considering function design, ontology design, individual agent design, facilitator design, society behavior and learning behavior design which can be plugged in any multi-agent based SHM system and tested with a case study.
Zhao et al [3] introduced an evaluation on a multi-agent system based structural health monitoring to validate the efficiency of the multi-agent technology, which can automatically choose sensing object, can self-organize the sensor network and discard useless sensor data, and can recognize three typical kinds of structure states which may indicate structural damages including impact load, joint failure, strain distribution change on every substructure and the edge area between two adjacent substructures.
Wu et al [4] presented a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring including the wireless strain gauge node, wireless PZT node and wireless USB station which can automatically allocate SHM tasks; self-organize the sensor network and aggregate different sensor information using six different SHM agents.
Son et al [5] considered support vector machine, linear discriminate analysis, k-nearest neighbors, and random forests algorithm as classifiers for fault diagnosis of machine using three types of signals involving vibration, current, and flux from induction motors.
Wang et al [6] identified a through-thickness hole in a stiffened composite panel using guided wave-based damage diagnostic algorithm, which is based on the probability of the presence of damage in the monitoring area estimated using correlation coefficients of Lamb wave signals from an active sensor network.
Shah and Ribakov [7] presented findings on nonlinear ultrasonic testing of concrete with different frequency transducers. As concrete is a high attenuation material, the change in fundamental amplitude or attenuation was found sensitive to using transducers with different frequency. The test results were also analyzed to find correlation between w/c, wave attenuation and higher harmonic generation based nonlinear parameters.
Overbey and Todd [8] evaluated input and output noise sensitivity on transfer entropy damage index as a feature for nonlinearity detection and linear damage identification, which requires the estimation of non-parametric one-, two-, and three-dimensional probability density functions.
Gul and Catbas [9] investigated statistical pattern recognition methods in the context of SHM using time series modeling, i.e. auto-regressive models, in conjunction with Mahalanobis distance-based outlier detection algorithms to identify different types of structural changes on different test structures.
Lopez and Klijn [10] used distance similarity matrix of dimensionally reduced data and shown ensembles perform better than any single-feature extraction method.
1. Distributed structural health monitoring with a smart sensor arrayMechanical Systems and Signal Processing, Volume 18, Issue 3, May 2004, Pages 555-572Timothy J. Johnson, Rebecca L. Brown, Douglas E. Adams, Mark Schiefer
2. Designing strategy for multi-agent system based large structural health monitoringExpert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 1154-1168Xia Zhao, Shenfang Yuan, Zhenhua Yu, Weisong Ye, Jun Cao
3. An evaluation on the multi-agent system based structural health monitoring for large scale structures Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 4900-4914 Xia Zhao, Shenfang Yuan, Hengbao Zhou, Hongbing Sun, Lei Qiu
4. Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring Expert Systems with Applications, In Press, Corrected Proof, Available online 7 July 2009 Jian Wu, Shenfang Yuan, Sai Ji, Genyuan Zhou, Yang Wang, Zilong Wang
5. Development of smart sensors system for machine fault diagnosisExpert Systems with Applications, Volume 36, Issue 9, November 2009, Pages 11981-11991Jong-Duk Son, Gang Niu, Bo-Suk Yang, Don-Ha Hwang, Dong-Sik Kang
6. Probability of the presence of damage estimated from an active sensor network in a composite panel of multiple stiffenersComposites Science and Technology, Volume 69, Issue 13, October 2009, Pages 2054-2063Dong Wang, Lin Ye, Ye Lu, Zhongqing Su
7. Non-destructive evaluation of concrete in damaged and undamaged statesMaterials & Design, Volume 30, Issue 9, October 2009, Pages 3504-3511A.A. Shah, Y. Ribakov
8. Effects of noise on transfer entropy estimation for damage detectionMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2178-2191L.A. Overbey, M.D. Todd
9. Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verificationsMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2192-2204Mustafa Gul, F. Necati Catbas
10. Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studiesMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2287-2300Israel Lopez, Nesrin Sarigul-Klijn
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