期刊
JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME
卷 131, 期 2, 页码 -出版社
ASME
DOI: 10.1115/1.3025827
关键词
autoregressive processes; condition monitoring; structural engineering computing; support vector machines; time series; vibration measurement; vibrations
The use of statistical methods for anomaly detection has become of interest to researchers in many subject areas. Structural health monitoring in particular has benefited from the versatility of statistical damage-detection techniques. We propose modeling structural vibration sensor output data using nonlinear time-series models. We demonstrate the improved performance of these models over currently used linear models. Whereas existing methods typically use a single sensor's output for damage detection, we create a combined sensor analysis to maximize the efficiency of damage detection. From this combined analysis we may also identify the individual sensors that are most influenced by structural damage.
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