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Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades./
作者:	Beale, Christopher.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	351 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13862944
ISBN:	9781392264485

Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades.
Beale, Christopher.

Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 351 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--University of Massachusetts Lowell, 2019.

This item must not be sold to any third party vendors.

The wind industry continues to experience high energy demands and technological advances yielding wind turbines both larger in quantity and size. Larger wind turbine blades consequently undertake harsher operational fatigue loads and environmental conditions further increasing the probability of failure in a component already prone to failure. Researchers have been investigating numerous damage detection techniques to provide a feasible structural health monitoring (SHM) approach applicable to wind turbine blade structures predominantly within the last two decades and still a viable solution is yet to be established. This research joins the pursuit by developing a novel acoustics-based SHM technique that relies on damage-induced changes in the acoustic energy transmitted through the walls of structural cavities as a potential solution for the operational condition monitoring of wind turbine blades. Additionally, the technique is easily extendable to other engineering structures with internal compartments such as cabins of automobiles, fuselages and wings of aircraft, or ducts and hollow supports.The proposed novel acoustics-based SHM technique is comprised of complementary passive and active damage detection approaches. The passive approach leverages naturally-generated acoustic phenomena from the operation of the structure to detect damage while the active approach leverages controlled user-generated acoustic excitations to detect damage, together achieving a low power and efficient condition monitoring solution. The passive approach is demonstrated on a wind turbine blade cross section exposed to flow-induced acoustic excitations, representative of an operational flow environment, to detect damage using internal acoustic pressure responses by monitoring the acoustic energy transmitted into the cavity. The active approach is demonstrated on several cavity structures and a ~46 m utility-scale wind turbine blade to detect damage using external acoustic pressure responses by monitoring the acoustic energy, generated by internal acoustic speakers, transmitted out of the cavity. The experimental investigations incorporate an exhaustive number of damage types, damage locations, damage severity levels, sensor positions, and environmental conditions representative of an operational wind turbine environment. The comprehensive experimental database established is used to develop advanced signal processing algorithms to facilitate the detection of damage using the proposed technique. Three of the algorithms are developed to detect damage from the acoustic pressure responses by quantifying differences in their power spectral density, wavelet packet nodal entropy, and peak-based characteristics of the cross correlation with respect to an established baseline. Additionally, an adaptive wavelet packet denoising algorithm is developed that is capable of efficiently removing ambient and transient noise from the acoustic pressure responses acquired while using the active approach. It is shown that the passive approach can detect all hole-type and crack-type damage as small as 0.32 cm in diameter and 1.27 cm in length, respectively, corresponding to 85% of all 30 damage scenarios considered with the developed spectral damage detection algorithms. Using the active approach, all developed algorithms are capable of detecting crack-type damage on the ~46 m utility-scale blade as small as 5.1 cm in length (~1/900th the length of the blade) from microphones over 15 m from the damage location, and 21.6 m using select algorithms. The damage detection performance is enhanced considerably with the adaptive wavelet packet denoising algorithm, which is capable of efficiently removing noise contamination from the utility-scale blade test data, ultimately improving the success rate by ~60% and reducing the false positive rate in select test cases. The results indicate that the test environment and excitation configuration of the active detection tests restrict damage-induced acoustic energy from being measured or transmitted through select damage scenarios because of unnatural acoustic reflections and the complex geometry of the blade's internal cavities and damage interfaces. Despite minor complications in select damage scenarios, damage is easily detected in other scenarios at all severity levels where the microphones are over twice as far from the damage using the same initial test design indicating minor adjustments to the test design could benefit the technique. Of all the algorithms, the cross correlation algorithm yields the most suitable feature capable of detecting all reasonable damage scenarios considered without any false detections, and is proven to be the least susceptible to operational and environmental conditions including temperature and noise. The overall damage detection performance of the passive and active damage detection approaches suggests the acoustics-based SHM technique is a viable condition monitoring solution for operational utility-scale wind turbine blades.

ISBN: 9781392264485Subjects--Topical Terms:

649730
Mechanical engineering.

Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades.
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245 1 0 $a Development of an Acoustics-Based Structural Health Monitoring Technique for the Monitoring of Operational Wind Turbine Blades.
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300 $a 351 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Inalpolat, Murat.
502 $a Thesis (Ph.D.)--University of Massachusetts Lowell, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a The wind industry continues to experience high energy demands and technological advances yielding wind turbines both larger in quantity and size. Larger wind turbine blades consequently undertake harsher operational fatigue loads and environmental conditions further increasing the probability of failure in a component already prone to failure. Researchers have been investigating numerous damage detection techniques to provide a feasible structural health monitoring (SHM) approach applicable to wind turbine blade structures predominantly within the last two decades and still a viable solution is yet to be established. This research joins the pursuit by developing a novel acoustics-based SHM technique that relies on damage-induced changes in the acoustic energy transmitted through the walls of structural cavities as a potential solution for the operational condition monitoring of wind turbine blades. Additionally, the technique is easily extendable to other engineering structures with internal compartments such as cabins of automobiles, fuselages and wings of aircraft, or ducts and hollow supports.The proposed novel acoustics-based SHM technique is comprised of complementary passive and active damage detection approaches. The passive approach leverages naturally-generated acoustic phenomena from the operation of the structure to detect damage while the active approach leverages controlled user-generated acoustic excitations to detect damage, together achieving a low power and efficient condition monitoring solution. The passive approach is demonstrated on a wind turbine blade cross section exposed to flow-induced acoustic excitations, representative of an operational flow environment, to detect damage using internal acoustic pressure responses by monitoring the acoustic energy transmitted into the cavity. The active approach is demonstrated on several cavity structures and a ~46 m utility-scale wind turbine blade to detect damage using external acoustic pressure responses by monitoring the acoustic energy, generated by internal acoustic speakers, transmitted out of the cavity. The experimental investigations incorporate an exhaustive number of damage types, damage locations, damage severity levels, sensor positions, and environmental conditions representative of an operational wind turbine environment. The comprehensive experimental database established is used to develop advanced signal processing algorithms to facilitate the detection of damage using the proposed technique. Three of the algorithms are developed to detect damage from the acoustic pressure responses by quantifying differences in their power spectral density, wavelet packet nodal entropy, and peak-based characteristics of the cross correlation with respect to an established baseline. Additionally, an adaptive wavelet packet denoising algorithm is developed that is capable of efficiently removing ambient and transient noise from the acoustic pressure responses acquired while using the active approach. It is shown that the passive approach can detect all hole-type and crack-type damage as small as 0.32 cm in diameter and 1.27 cm in length, respectively, corresponding to 85% of all 30 damage scenarios considered with the developed spectral damage detection algorithms. Using the active approach, all developed algorithms are capable of detecting crack-type damage on the ~46 m utility-scale blade as small as 5.1 cm in length (~1/900th the length of the blade) from microphones over 15 m from the damage location, and 21.6 m using select algorithms. The damage detection performance is enhanced considerably with the adaptive wavelet packet denoising algorithm, which is capable of efficiently removing noise contamination from the utility-scale blade test data, ultimately improving the success rate by ~60% and reducing the false positive rate in select test cases. The results indicate that the test environment and excitation configuration of the active detection tests restrict damage-induced acoustic energy from being measured or transmitted through select damage scenarios because of unnatural acoustic reflections and the complex geometry of the blade's internal cavities and damage interfaces. Despite minor complications in select damage scenarios, damage is easily detected in other scenarios at all severity levels where the microphones are over twice as far from the damage using the same initial test design indicating minor adjustments to the test design could benefit the technique. Of all the algorithms, the cross correlation algorithm yields the most suitable feature capable of detecting all reasonable damage scenarios considered without any false detections, and is proven to be the least susceptible to operational and environmental conditions including temperature and noise. The overall damage detection performance of the passive and active damage detection approaches suggests the acoustics-based SHM technique is a viable condition monitoring solution for operational utility-scale wind turbine blades.
590 $a School code: 0111.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Acoustics. $3 879105
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710 2 $a University of Massachusetts Lowell. $b Mechanical Engineering. $3 2095290
773 0 $t Dissertations Abstracts International $g 80-12B.
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791 $a Ph.D.
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13862944