Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring

abstract: Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to...

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Other Authors: Huff, Daniel William (Author)
Format: Doctoral Thesis
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.20974
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spelling ndltd-asu.edu-item-209742018-06-22T03:04:38Z Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring abstract: Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage. Dissertation/Thesis Huff, Daniel William (Author) Papandreou-Suppappola, Antonia (Advisor) Kovvali, Narayan (Committee member) Chakrabarti, Chaitali (Committee member) Chattopadhyay, Aditi (Committee member) Arizona State University (Publisher) Electrical engineering Aerospace engineering Mechanical engineering adaptive sensing hidden Markov models Structural Health Monitoring time-frequency eng 144 pages Ph.D. Electrical Engineering 2013 Doctoral Dissertation http://hdl.handle.net/2286/R.I.20974 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2013
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Electrical engineering
Aerospace engineering
Mechanical engineering
adaptive sensing
hidden Markov models
Structural Health Monitoring
time-frequency
spellingShingle Electrical engineering
Aerospace engineering
Mechanical engineering
adaptive sensing
hidden Markov models
Structural Health Monitoring
time-frequency
Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
description abstract: Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage. === Dissertation/Thesis === Ph.D. Electrical Engineering 2013
author2 Huff, Daniel William (Author)
author_facet Huff, Daniel William (Author)
title Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
title_short Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
title_full Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
title_fullStr Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
title_full_unstemmed Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
title_sort adaptive methods within a sequential bayesian approach for structural health monitoring
publishDate 2013
url http://hdl.handle.net/2286/R.I.20974
_version_ 1718700289535508480