TY - GEN
T1 - Structural health monitoring for ship structures
AU - Farrar, C.
AU - Park, G.
AU - Angel, M.
AU - Bement, M.
AU - Salvino, L.
PY - 2009
Y1 - 2009
N2 - Currently the Office of Naval Research is supporting the development of structural health monitoring (SHM) technology for U.S. Navy ship structures. This application is particularly challenging because of the physical size of these structures, the widely varying and often extreme operational and environmental conditions associated with these ships' missions, lack of data from known damage conditions, limited sensing that was not designed specifically for SHM, and the management of the vast amounts of data that can be collected during a mission. This paper will first define a statistical pattern recognition paradigm for SHM by describing the four steps of 1.) Operational Evaluation, 2.) Data Acquisition, 3. Feature Extraction, and 4.) Statistical Classification of Features as they apply to ship structures, Note that inherent in the last three steps of this process are additional tasks of data cleansing, compression, normalization and fusion. The presentation will discuss ship structure SHM challenges in the context of applying various SHM approaches to sea trials data measured on an aluminum multi-hull high-speed ship, the HSV-2 Swift. To conclude, the paper will discuss several outstanding issues mat need to be addressed before SHM can make the transition from a research topic to actual field applications on ship structures and suggest approaches for addressing these issues.
AB - Currently the Office of Naval Research is supporting the development of structural health monitoring (SHM) technology for U.S. Navy ship structures. This application is particularly challenging because of the physical size of these structures, the widely varying and often extreme operational and environmental conditions associated with these ships' missions, lack of data from known damage conditions, limited sensing that was not designed specifically for SHM, and the management of the vast amounts of data that can be collected during a mission. This paper will first define a statistical pattern recognition paradigm for SHM by describing the four steps of 1.) Operational Evaluation, 2.) Data Acquisition, 3. Feature Extraction, and 4.) Statistical Classification of Features as they apply to ship structures, Note that inherent in the last three steps of this process are additional tasks of data cleansing, compression, normalization and fusion. The presentation will discuss ship structure SHM challenges in the context of applying various SHM approaches to sea trials data measured on an aluminum multi-hull high-speed ship, the HSV-2 Swift. To conclude, the paper will discuss several outstanding issues mat need to be addressed before SHM can make the transition from a research topic to actual field applications on ship structures and suggest approaches for addressing these issues.
UR - http://www.scopus.com/inward/record.url?scp=84944672732&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84944672732
T3 - Structural Health Monitoring 2009: From System Integration to Autonomous Systems - Proceedings of the 7th International Workshop on Structural Health Monitoring, IWSHM 2009
SP - 1970
EP - 1977
BT - Structural Health Monitoring 2009
A2 - Chang, Fu-Kuo
PB - DEStech Publications
T2 - 7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM 2009
Y2 - 9 September 2009 through 11 September 2009
ER -