CAAP: Embedded Passive RF Tags Towards Intrinsically Locatable Buried Plastic Material

Mondal, Saikat; Kumar, Deepak; Chahal, Prem; Deng, Yiming

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CAAP: Embedded Passive RF Tags Towards Intrinsically Locatable Buried Plastic Material

2019-01-30
By Mondal, Saikat ; Kumar, Deepak ; Chahal, Prem

File Language:

English

[PDF - 12.35 MB]

Details

Creators:

Mondal, Saikat ; Kumar, Deepak ; Chahal, Prem ; Deng, Yiming
Corporate Creators:

University of Colorado at Denver ; Michigan State University
Corporate Contributors:

United States. Department of Transportation. Pipeline and Hazardous Materials Safety Administration ; United States. Department of Transportation. Pipeline and Hazardous Materials Safety Administration. Competitive Academic Agreement Program (CAAP)
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

United States. Dept. of Transportation. Pipeline and Hazardous Materials Safety Administration
Abstract:

Accurate and reliable locating, identifying and characterizing the buried plastic pipes from the ground surface in reducing the likelihood of hit them is critical and imperative to reduce the pipeline incidents. In this collaborative research, a new harmonic radar (frequency doubling) mechanism for smart RF tags design that can detect plastic pipes deeply buried in various soils conditions will be investigated, achieved through efficient tags and highly sensitive readers design, and coupled with intelligent signal processing. The proposed low-cost, small thin-film form passive RF tags can directly be embedded in plastic pipes. It will be able to withstand high temperature processing of plastics and stress involved with horizontal tunneling/drilling of buried pipes. The embedded RF tags have the capability to not only precisely locate the buried plastic pipes, but also have integrated sensing functionality, which can measure the strain-stress changes in the plastic materials. Finally, the vast amount of acquired sensing data from individual tags will be integrated to the advanced signal processing for better data categorization and mining. An innovative prognostics framework for better asset life-cycle management will be developed.
Format:

PDF
Funding:

DTPH5615HCAP08L
Download URL:

https://rosap.ntl.bts.gov/view/dot/56140/dot_56140_DS1.pdf
File Type:

[PDF - 12.35 MB]
Collection(s):

PHMSA Collection
Main Document Checksum:

urn:sha256:74416ab7f763c21e2c0bd6467b0179dd1711e0fa20f7306449dacb82f95e6b45

File Language:

English

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