Sage Journals: Discover world-class research

Abstract

Long-term SHM systems need to control the scale of monitoring data storage and reduce the maintenance cost of sensor failures. Inspired by human memory, an intelligent structural state retrieval model is presented. First, the mental model of human memory is introduced and then the structural state recognition model and recall model are established. Methods of calculating familiarity values for recognition and similarity values for recall are discussed for the model. An experiment on strain monitoring was carried out to show the implementation process while demonstrating the effectiveness of the model. It is shown that the structural state recognition model can discard a large amount of redundant information and the structural state recall model can restore the missing data of failed sensors.

Keywords

structural health monitoring bio-inspired human memory sensor failure.

Get full access to this article

View all access options for this article.

References

Atkinson, R.C. and Shiffrin, R.M. 1968. ‘‘Human Memory: a Proposed System and its Control Processes,’’ In: Spence, K.W. (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory, pp. 89-195, Academic Press, New York.

Gillund, G. and Shiffrin, R.M. 1984. ‘‘A Retrieval Model for Both Recognition and Recall,’’ Psychological Review, 91:1-67.

Hou, Z.K. , Hera, A. and Shinde, A. 2006. ‘‘Wavelet-based Structural Health Monitoring of Earthquake Excited Structures,’’ Computer-aided Civil and Infrastructure Engineering, 21(4):268-279.

Keiichi, O. and Michihito, S. 2006. ‘‘Peak Hold Sensor System for Post-earthquake Structural Health Monitoring,’’ In: The 23rd Sensor Symposium on Sensors, Micromachines, and Applied Systems, Vol. 23, pp. 541-544, Takamatsu , Japan.

Klein, L.A. 2004. Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, SPIE Press, Bellingham, WA.

Liu, S.P. and Cheng, L. 2006. ‘‘Efficient Data Compression in Wireless Sensor Networks for Civil Infrastructure Health Monitoring,’’ In: 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, Vol. 3, pp. 823-829, Reston, VA, USA.

Lu, P. , Wipf, T.J. , Phares, B.M. and Doornink, J.D. 2007. ‘‘A Bridge Structural Health Monitoring and Data Mining System,’’ In: Proceedings of the (2007) Mid-Continent Transportation Research Symposium, pp. 1-15, Iowa , USA.

Lynch, J.P. , Sundarajan, A. , Law, K.H. , Kiremidjian, A.S. , Kenny, T. and Carryer, E. 2003. ‘‘Embedment of Structural Monitoring Algorithms in a Wireless Sensing Unit,’’ Structural Engineering and Mechanics, 15(3):1-13.

Nagayama, T. , Spencer, B.F. , Agha, G.A. and Mechitov, K.A. 2006. ‘‘Model-based Data Aggregation for Structural Monitoring Employing Smart Sensors,’’ In: 3rd International Workshop on Networked Sensing Systems , pp. 203-210, Chicago , USA.

10.

Pei, J.S. , Kapoor, C. , Graves-Abe, T.L. , Sugeng, Y. and Lynch, J.P. 2004. ‘‘Critical Design Parameters and Operating Conditions of Wireless Sensor Units for Structural Health Monitoring,’’ In: Proceedings of 42nd Allerton Conference on Communication, Control and Computing, Allerton, USA.

11.

Raaijmakers, J.G.W. and Shiffrin, R.M. 1981. ‘‘Search of Associative Memory, ’’ Psychological Review, 88: 93-134.

12.

Yuen, K.V. and Katafygiotis, L.S. 2006. ‘‘Substructure Identification and Health Monitoring Using Noisy Response Measurements Only,’’ Computer-aided Civil and Infrastructure Engineering, 21(4):280-291.

Intelligent Structural State Retrieval Model Inspired from Human Memory

Abstract

Keywords

Get full access to this article

References