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Damage detection using outlier analysis
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AI
DOI：10.1006/jsvi.1999.2514.png)
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Abstract
En 中文
This paper constitutes a study of a statistical method for damage detection. The lowest level of fault detection is considered so that the methods are simply required to signal deviations from normal condition; i.e., the problem is one of novelty detection. In this paper, the concept of discordancy from the statistical discipline of outlier analysis is used to signal deviance from the norm. The method is demonstrated on four case studies of engineering interest: one simulation, two pseudo-experimental and one experimental. (C) 2000 Academic Press.
Journal
IF:
4.9
Papers:
1.7W
Citations:
4.8W
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