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Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Peng, Zhaoqiang (2020) Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments.
Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection.
Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability.
The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Peng, Zhaoqiangzhp7@pitt.eduzhp70000-0002-2967-9251
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChen, P.
Committee MemberMao,
Committee MemberLi,
Committee MemberXiong,
Committee MemberOhodnicki, Paul
Date: 30 July 2020
Defense Date: 22 July 2020
Approval Date: 28 September 2020
Submission Date: 5 August 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 121
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Distributed Ultrasound Sensing; Machine Learnings; Fiber Optics
Date Deposited: 28 Sep 2020 20:00
Last Modified: 28 Sep 2020 20:00


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