Structural health monitoring for ship structures

Conference · Thu Jan 01 04:00:00 EST 2009

OSTI ID:990775

Farrar, Charles ^[1]; Park, Gyuhae ^[1]; Angel, Marian ^[1]; Bement, Matthew ^[1]; Salvino, Liming ^[2]

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1. Los Alamos National Laboratory
2. NSWC, CADEROCK

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 that 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.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: DOE
• DOE Contract Number: AC52-06NA25396
• OSTI ID: 990775
• Report Number(s): LA-UR-09-04362; LA-UR-09-4362
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
ALUMINIUM
CLASSIFICATION
COMPRESSION
DATA ACQUISITION
EVALUATION
MANAGEMENT
MONITORING
PATTERN RECOGNITION