In this paper, we propose to use a data modeling technology, Factor Analysis, in the application of complex system fault diagnosis and failure prognosis. Factor Analysis captures the dominant dependency underlying observable measurements of physical systems, and is sensitive to their changes, as demonstrated by the preliminary experimental results on two real-world datasets. Comparison studies show that Factor Analysis has advantages over two related techniques,Principal Component Analysis and K-Means.
How to Cite
battery management systems, fault detection, factor analysis, principal component analysis, K-means
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