[논문]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

doi:10.7779/jksnt.2016.36.2.93

[국내논문] Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment 원문보기

비파괴검사학회지 = Journal of the Korean Society for Nondestructive Testing, v.36 no.2, 2016년, pp.93 - 101

Choi, Mijin (Department of Aerospace Engineering & LANL-CBNU Engineering Institute, Chunbuk National University) , Jung, Hwee Kwon (School of Mechanical Engineering, Chonnam National University) , Taylor, Stuart G. (The Engineering Institute, Los Alamos National Laboratory) , Farinholt, Kevin M. (The Engineering Institute, Los Alamos National Laboratory) , Lee, Jung-Ryul (Department of Aerospace Engineering, KAIST) , Park, Gyuhae (School of Mechanical Engineering, Chonnam National University)

Abstract ▼ AI-Helper

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.

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문제 정의

In this paper, a time-series analysis to extract damage sensitive features for CX-100 wind turbine systems was addressed. The use of damage extraction using time-series analysis has been shown to be effective in detecting damage during the fatigue test, as the method identified the fatigue damage before a visual crack was observed.
The goal of this study is to assess timedomain data measured for embedded piezoelectric active-sensors as a way to nondestructively monitor the health of wind turbines. This study uses the first four statistical moments and a normality test to assess the condition of the wind turbine blade under fatigue loads.

제안 방법

The blade was initially excited at 25% of its design load, and then the load was steadily increased until the blade failed as shown in Fig. 2. To induce the fatigue loading, a UREX (universal resonant excitation) hydraulic actuator (load saddle) was installed at the 1.6 m spanwise station with a single ballast saddle located at the 6.75 m from the root, respectively, and extra masses were added on the first load saddle at 1.6 m, leading to increase of mass from 1,284 lbs initially to 1,416, 1,548, 1,680 lbs respectively. For more details on the experimental set-up, see References [13,14].
In this study, damage features were extracted using residual errors between measured data and ARX model predicted data. Fig.
This study reviewed and applied the timeseries analysis for SHM to data sets acquired from the full scale CX-100 fatigue test which was performed from 08/11/2011 to 11/09/2011. With the measured piezoelectric active-sensing data, basic statistics, which include first four statistical moments and normality test, were used to extract damage sensitive features.
The goal of this study is to assess timedomain data measured for embedded piezoelectric active-sensors as a way to nondestructively monitor the health of wind turbines. This study uses the first four statistical moments and a normality test to assess the condition of the wind turbine blade under fatigue loads. The underline assumption of this approach is that the high-frequency excitation of piezoelectric transducer is sensitive enough to detect incipient crack in a structure and insensitive to operational vibrations.
This study reviewed and applied the timeseries analysis for SHM to data sets acquired from the full scale CX-100 fatigue test which was performed from 08/11/2011 to 11/09/2011. With the measured piezoelectric active-sensing data, basic statistics, which include first four statistical moments and normality test, were used to extract damage sensitive features. Observations from these results showed that the skewness, kurtosis, and normal distribution of IS#5 started to be deviated from 10/19/2011.

대상 데이터

The test set-up is shown in Fig. 1. The specimen is 100 kW blade, referred to as CX-100, which contains a full-length carbon spar cap infused with the rest of the blade as part of the normal glass blade fabrication process. This 9 m turbine blade was mounted to the 7 ton test stand and harmonically excited by its first natural frequency at 1.
All transducers utilized in this study were APC International D-0.500-0.020-851 PZT transducers, fabricated using the APC’s 851 material.

이론/모형

Therefore, it is important to determine the optimal model order to the ARX model development. In order to computing the optimal model order, the root mean squared error (RMSE) technique was implemented in this study. In the ARX model, the RMSE is a measure of the differences between the values estimated by model and the values actually measured.

성능/효과

One conclusion could be drawn that, without using any advanced signal processing techniques to extract a damage sensitive feature, the basic four statistical moments could be efficiently used to detect and locate structural damage. This is mainly because the frequency range used to interrogate structural components is high and sensitive enough to detect and locate structural damage, and insensitive to operational vibration, i.
Therefore, in this section, we can draw the conclusion that the blade had damage initiation on 10/19/2011, which is much earlier than the damage was visually identified. The results of this method are similar to those obtained by using less intensive computations, the first four statistical moments and normality tests, meaning that the basic time-series signal processing technique can be efficiently used to detect structural damage and also suggests the capability of providing a real-time health monitoring system for the wind-turbine system.

참고문헌 (15)

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H. Sohn, C. R. Farra, N. Hunter and K. Worden, "Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surfaceeffect fast patrol boat," LA-13761-MS, The Enginerring Institute, Los Alamos National Laboratory, NM (US) (2001)
E. Figueiredo, G. Park, J. Figueriras, C. Farrar and K. Worden, "Structural health monitoring algorithm comparisons using standard data sets," No. LA-14393, The Engineering Institute, Los Alamos National Laboratory (2009)
S. G. Taylor, K. Farinholt, M. Choi, H. Jeong, J. Jang, G. Park, J. R. Lee and M. D. Todd, "Incipient crack detection in a composite wind turbine rotor blade," Journal of Intelligent Material Systems and Structures, Vol. 23, No. 5, pp. 613-620 (2013)
N. Dervilis, M. Choi, S. G. Taylor, J. R. Barthorpe, G. Park, C. R. Farrar and K. Worden "On damage diagnosis for a wind turbine blade using pattern recognition," Journal of Sound and Vibration, Vol. 333, pp. 1833-1850 (2014)

상세보기
N. Dervilis, M. Choi, I. Antoniadou, K. Farinholt, S. G. Taylor and R. Barthorpe, "Novelty detection applied to vibration data from a CX100 wind turbine blade under fatigue loading," Journal of Physics Conference Series 382(1): 012047 (2012)

상세보기
H. Sun, Y. Zi and Z. He, "Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold," Applied Acoustics, Vol. 77, pp. 122-129 (2014)

상세보기
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상세보기
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상세보기
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W. Y. Liu, B. P. Tang, J. G. Han, X. N. Lu, N. N. Hu and Z. Z. He, "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: a review," Renewable and Sustainable Energy Reviews, Vol. 44, pp. 466-472 (2015)

상세보기
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상세보기
S. G. Taylor, G. Park, K. F. Farinholt, and M. D. Todd, "Fatigue crack detection performance comparison in a composite wind turbine rotor blade," International Journal of Structural Health Monitoring, Vol. 12, No. 3, pp. 252-262 (2013)

상세보기
K. M. Farinhot, S. G. Taylor, G. Park and Cutt M. Ammerman, "Full-scale fatigue tests of CX-100 wind turbine blades. Part I: testing," SPIE Smart Structures+ Nondestructive Evaluation and Health Monitoring, International Society for Optics and Photonics, p. 83430P-83430P-8 (2012)
E. Figueiredo, G. Park, K. M. Farinholt, C. R. Farrar, and J. R. Lee, "Use of time-series predictive models for piezoelectric active-sensing in structural health monitoring applications," ASME Journal of Vibration and Acoustics, Vol. 134, No. 4, p. 041014 (2012)

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[국내논문] Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment 원문보기

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[국내논문] Analysis of Time Domain Active Sensing Data from CX-100 Wind Turbine Blade Fatigue Tests for Damage Assessment 원문보기

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