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Research Papers

Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications

[+] Author and Article Information
Eloi Figueiredo

Gyuhae Park1

Kevin M. Farinholt, Charles R. Farrar

 The Engineering Institute, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Jung-Ryul Lee

 Department of Aerospace Engineering and LANL-CBNU Engineering Institute Korea, ChonBuk National University, Jeonju, 561-756, South Korea

1

Corresponding author.

J. Vib. Acoust 134(4), 041014 (Jun 01, 2012) (10 pages) doi:10.1115/1.4006410 History: Received September 02, 2010; Revised February 27, 2012; Published June 01, 2012; Online June 01, 2012

In this paper, time domain data from piezoelectric active-sensing techniques is utilized for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, their ability to provide known repeatable inputs for active-sensing approaches to SHM makes the development of SHM signal processing algorithms more efficient and less susceptible to operational and environmental variability. However, to date, most of these techniques have been based on frequency domain analysis, such as impedance-based or high-frequency response functions-based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or other analysis for damage-sensitive feature extraction. Therefore, this study investigates the use of a time-series predictive model which utilizes the data obtained from piezoelectric active-sensors. In particular, time series autoregressive models with exogenous inputs are implemented in order to extract damage-sensitive features from the measurements made by piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were artificially imposed. The performance of this approach is compared to that of analysis based on frequency response functions and its capability for SHM is demonstrated.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 11

Plot of the αj parameters projected onto the first two principal components

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Figure 1

The composite plate used for the test

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Figure 2

Experimental setup of the composite plate

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Figure 3

Locations of MFC/PZT and the impact

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Figure 4

Ultrasonic scan of the plate with induced delamination

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Figure 5

Ratio of the variance of the signal (input/output) for each test

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Figure 6

Minimal eigeinvalue for increasing ARX model orders (assuming p = q)

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Figure 7

Comparison of the measured and estimated time series using the ARX(30,30) model fit to the baseline condition data

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Figure 8

Comparison of the measured and estimated time series using the AR(300) model fit to the baseline condition data

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Figure 9

αj parameters for the undamaged condition and after Impact 2 from ARX (30,30) model

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Figure 10

βj parameters for the undamaged condition and after Impact 2 from ARX (30,30) model

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Figure 12

Plot of the βj parameters projected onto the first two principal components

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Figure 13

MSDs using the αj parameters from the baseline condition as the training data (normal condition, NC)

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Figure 14

MSDs using the αj parameters from the baseline condition and state condition after Impacts 1 and 2 as the training data (normal condition)

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