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Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment
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 Title & Authors
Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment
Choi, Mijin; Jung, Hwee Kwon; Taylor, Stuart G.; Farinholt, Kevin M.; Lee, Jung-Ryul; Park, Gyuhae;
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This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.
Structural Health Monitoring (SHM);Time Series Analysis;Piezoelectric Active Sensor;ARX Model;
 Cited by
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