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Novelty detection using auto-associative neural network.

DE2001782786

Publication Date	2001
Personal Author	Sohn, H.; Worden, K.; Farrar, C.
Page Count	8
Abstract	The primary objective of novelty detection is to examine if a system significantly deviates from the initial baseline condition of the system. In reality, the system is often subject to changing environmental and operation conditions affecting its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and humidity. 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	Diagnosis Humidity Neural networks Boundary conditions Detection
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	63 - Detection & Countermeasures 95F - Bionics & Artificial Intelligence
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

Novelty detection using auto-associative neural network.

Novelty detection using auto-associative neural network.
DE2001782786

Publication Date	2001
Personal Author	Sohn, H.; Worden, K.; Farrar, C.
Page Count	8
Abstract	The primary objective of novelty detection is to examine if a system significantly deviates from the initial baseline condition of the system. In reality, the system is often subject to changing environmental and operation conditions affecting its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and humidity. 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	Diagnosis Humidity Neural networks Boundary conditions Detection
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	63 - Detection & Countermeasures 95F - Bionics & Artificial Intelligence
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