{"dcterms:modified":"2026-01-12","dcterms:creator":"PolyU Research Data Repository","@type":"ore:ResourceMap","schema:additionalType":"Dataverse OREMap Format v1.0.0","dvcore:generatedBy":{"@type":"schema:SoftwareApplication","schema:name":"Dataverse","schema:version":"6.1","schema:url":"https://github.com/iqss/dataverse"},"@id":"https://researchdata.lib.polyu.edu.hk/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.60933/PRDR/HJYNZB","ore:describes":{"grantNumber":[{"citation:grantNumberAgency":"Innovative Technology Fund by HKSAR Government","citation:grantNumberValue":"PRP/014/19FX"},{"citation:grantNumberAgency":"The Hong Kong and China Gas Company Limited","citation:grantNumberValue":"PRP/014/19FX"}],"citation:dateOfCollection":{"citation:dateOfCollectionStart":"2021-01-20"},"author":[{"citation:authorName":"YU, Samuel","citation:authorAffiliation":"The Hong Kong Polytechnic University","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0002-5615-9956"},{"citation:authorName":"CHUNG, Winnie Wai-sze","citation:authorAffiliation":"The Hong Kong Polytechnic University"},{"citation:authorName":"LAU, Tom Chun-wai","citation:authorAffiliation":"The Hong Kong Polytechnic University","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0003-2144-659X"},{"citation:authorName":"LAI, Wallace Wai-lok","citation:authorAffiliation":"The Hong Kong Polytechnic University","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0002-0696-6741"},{"citation:authorName":"SHAM","citation:authorAffiliation":"Janet Fung Chu","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0001-6424-5734"},{"citation:authorName":"HO, Chun Yiu","citation:authorAffiliation":"The Hong Kong and China Gas Company Limited"}],"citation:keyword":[{"citation:keywordValue":"Flat Bottomed Hole"},{"citation:keywordValue":"Pulsed Thermography","citation:keywordVocabulary":"PT"}],"citation:datasetContact":[{"citation:datasetContactName":"YU, Samuel","citation:datasetContactAffiliation":"The Hong Kong Polytechnic University","citation:datasetContactEmail":"samuel-yu.yu@connect.polyu.hk"},{"citation:datasetContactName":"LAI, Wallace Wai-lok","citation:datasetContactAffiliation":"The Hong Kong Polytechnic University","citation:datasetContactEmail":"wallace.wai.lok.lai@polyu.edu.hk"}],"citation:topicClassification":[{"citation:topicClassValue":"Infrared Thermography","citation:topicClassVocab":"IRT"},{"citation:topicClassValue":"Non-destructive Testing","citation:topicClassVocab":"NDT"}],"software":{"citation:softwareName":"ResearchIR"},"publication":{"publicationCitation":"Samuel Yu, Winnie Wai-sze Chung, Tom Chun-wai Lau, Wallace Wai-lok Lai, Janet Fung Chu Sham, Chun Yiu Ho,\nLaboratory validation of in-pipe pulsed thermography in the rapid assessment of external pipe wall thinning in buried metallic utilities,\nNDT & E International,\nVolume 135,\n2023,\n102791,\nISSN 0963-8695,\nhttps://doi.org/10.1016/j.ndteint.2023.102791.\n(https://www.sciencedirect.com/science/article/pii/S0963869523000063)\nAbstract: This study characterized the in-pipe thermal signature of external pipewall thinning in steel pipes, a common problem that is caused by external corrosion in hostile underground environment. A model system was prepared to imitate the underground environment by milling several holes of various sizes and residual thicknesses into a mild steel plate. Wall thinning was investigated using active infrared thermography. The non-defective side of the steel plate was heated to 27.4 °C through the application of a thermal energy pulse while the ambient temperature was 22°C. Thermograms were captured inside the pipe at a frequency of 0.02 seconds for 5 min. The images of the thinned surface were processed in two steps. First, the peak contrast time algorithm was used to estimate the residual thickness. Second, Gaussian adaptive thresholding was used to estimate the size of the holes. The maximum observable defects had a diameter of 5 mm and a residual thickness of 3 mm. The type of defect interface (steel–sand or steel–air) had no significant effect on the estimation of residual thickness or size. This study developed a rapid approach in classifying defect's residual thickness by only utilizing two well-known parameters from infrared images – defect's peak thermal contrast and estimated area. Thus, the feasibility of non-destructive, in-pipe, quantitative IR thermographic analysis of buried metal pipelines is demonstrated.\nKeywords: Infrared thermography; Residual thickness estimation; Size estimation; Peak thermal contrast; Peak contrast time; Adaptive thresholding","publicationIDType":"doi","publicationIDNumber":"10.1016/j.ndteint.2023.102791"},"citation:dsDescription":{"citation:dsDescriptionValue":"

Abstract

\n

This dataset presents a thermal imaging dataset from mild steel sample that were inspected by pulsed thermography with the goal of detecting and characterizing external corrosion of metallic underground utilites (manually milled flat bottom holes representing corroded regions). The pulsed thermography experiment was applied to two mild steel plates with the dimensions of 150 mm (length) × 150 mm (width) × 10 mm (thick). The first plate contains 11 circular milled flat-bottomed holes with residual thicknesses of 1 mm 3 mm and 5 mm and lateral size of 5 mm, 10 mm, 15 mm and 20 mm while the second plates contains 9 rectangular milled flat-bottomed holes with residual thickness of 3 mm, 6 mm and 8 mm, width of 2 mm, 4 mm, 7 mm and uniform height of 20 mm. 6 sets of data were collected for both plates with half of them having steel-air interface and the other half having steel-sand interface. A pair of ZOOM PRO HD Elinchrom Digital RX photographic flashes (3 kJ per flash lamp) were used to generate the heat pulse (4 ms duration), an A655sc FLIR infrared camera using ResearchIR software to record the thermal images 450mm from the mild steel sample. Finally, the dataset proposed consists of 6 sequences of approximately 8000 images of 640 × 480 pixels each.

\n\n

Methods 

\n

Test Sample

\n

This dataset is collected using pulsed thermography method. Sequential IR image data were obtained using a pulsed-heating TNDT system. In the experiment, two 150 mm (length) × 150 mm (width) × 10 mm (thick) mild steel plate was machine-milled with 11 and 9 flat-bottom holes, respectively. The plate milled with circular holes had varying diameters (20 mm, 15 mm, 10 mm, 5 mm) and residual thicknesses (1 mm, 3 mm, 5 mm) while the plate milled with rectangular holes had varying width (2 mm, 4 mm, 7 mm) and residual thickness (3 mm, 6 mm, 8 mm) to imitate the various defects in a pipe wall. To model the environment of a buried metallic pipe, 3 sets of data were collected while the holes were filled with sand.

\n

Instrumentation

\n

A pair of flash lamps, which were directed towards the rear surface of the plate, was used for excitation (at t = 0 s). The plate was painted with a thin layer of black flat paint so that its emissivity was as close as possible to that of a blackbody. The change in temperature of the surface was monitored by a high-speed, long-wave infrared camera (7–14 μm) with a 640 × 480 pixel microbolometer at a frame rate of 50 Hz for 2.5 minutes after the application of the pulse. The instrument specifications and parameters used for the acquisition of the thermogram are provided in the table below.

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n

Experimental Equipment

\n 		\n

Acquisition Parameters

\n
\n

Thermal stimulation:

\n 		\n

Sampling rate

\n 		\n

50 Hz

\n
\n

Photographic Flashes: ELINCHROM ZOOM Pro HD

\n
\n

Pulse duration: 4 ms thermal pulse,

\n
\n

Deposited energy: 3 kJ/flash

\n

(Total energy deposited 6 kJ)

\n 		\n

Acquisition duration

\n 		\n

160 s

\n
\n

Thermographic monitoring

\n 		\n

Time interval

\n 		\n

20 ms

\n
\n

FLIR A655sc, FOV 25°,

\n
\n

640 × 480 pixel microbolometer,

\n
\n

16-bit data, NETD: <30 mK,

\n

spectral range: 7.5-14.0 µm

\n 		\n

Total number of frames

\n 		\n

8000

\n
\n

 

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The experiment was performed from the front faces of the specimens with two types of boundary interfaces at the back of the specimen (sand-steel or air-steel) and each sequence was labelled and saved into an independent file. \n\nDescription of the data and file structure\nEvery .zip file contains 1000 .csv files. They are labelled as MS-facq-50Hz-interfaceMaterial-shapeOfHoles-collectionSetNumber_Img-frames. 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