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Details

Damage diagnosis considering changing evnironmental conditions.

DE2001780519

Publication Date	2001
Personal Author	Sohn, H.; Worder, K.; Farrar, C.
Page Count	12
Abstract	The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.
Keywords	Boundary conditions Detection Diagnosis Moisture Neural networks
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	68 - Environmental Pollution & Control
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200125
Contract Number	W-7405-ENG-36

Damage diagnosis considering changing evnironmental conditions.

Damage diagnosis considering changing evnironmental conditions.
DE2001780519

Publication Date	2001
Personal Author	Sohn, H.; Worder, K.; Farrar, C.
Page Count	12
Abstract	The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.
Keywords	Boundary conditions Detection Diagnosis Moisture Neural networks
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	68 - Environmental Pollution & Control
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200125
Contract Number	W-7405-ENG-36