Data-driven analysis of ultrasonic pressure tube inspection data

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Pressure tubes are critical components of the CANDU reactors and other pressurized heavy water type reactors, as they contain the nuclear fuel and the coolant. Manufacturing flaws, as well as defects developed during the in-service operation, can lead to coolant leakage and can potentially damage the reactor. The current inspection process of these flaws is based on manually analyzing ultrasonic data received from multiple probes during planned, statutory outages. Recent advances on ultrasonic inspection tools enable the provision of high resolution data of significantly large volumes. This is highlighting the need for an efficient autonomous signal analysis process. Typically, the automation of ultrasonic inspection data analysis is approached by knowledge-based or supervised data-driven methods. This work proposes an unsupervised data-driven framework that requires no explicit rules, nor individually labeled signals. The framework follows a two-stage clustering procedure that utilizes the DBSCAN density-based clustering algorithm and aims to provide decision support for the assessment of potential defects in a robust and consistent way. Nevertheless, verified defect dimensions are essential in order to assess the results and train the framework for unseen defects. Initial results of the implementation are presented and discussed, with the method showing promise as a means of assessing ultrasonic inspection data.

Conference

Conference10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies
Abbreviated titleNPIC and HMIT 2017
CountryUnited States
CitySan Francisco
Period11/06/1715/06/17
Internet address

Fingerprint

Inspection
Ultrasonics
Defects
Coolants
Heavy water
Signal analysis
Nuclear fuels
Outages
Clustering algorithms
Automation

Keywords

  • CANDU
  • pressure tubes
  • ultrasonic inspection
  • machine learning
  • reactors
  • nuclear fuel

Cite this

Zacharis, P., West, G. M., Dobie, G., Lardner, T., & Gachagan, A. (2017). Data-driven analysis of ultrasonic pressure tube inspection data. Paper presented at 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, United States.
Zacharis, P. ; West, G. M. ; Dobie, G. ; Lardner, T. ; Gachagan, A. / Data-driven analysis of ultrasonic pressure tube inspection data. Paper presented at 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, United States.
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Zacharis, P, West, GM, Dobie, G, Lardner, T & Gachagan, A 2017, 'Data-driven analysis of ultrasonic pressure tube inspection data' Paper presented at 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, United States, 11/06/17 - 15/06/17, .

Data-driven analysis of ultrasonic pressure tube inspection data. / Zacharis, P.; West, G. M.; Dobie, G.; Lardner, T.; Gachagan, A.

2017. Paper presented at 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, United States.

Research output: Contribution to conferencePaper

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AU - Zacharis, P.

AU - West, G. M.

AU - Dobie, G.

AU - Lardner, T.

AU - Gachagan, A.

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M3 - Paper

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Zacharis P, West GM, Dobie G, Lardner T, Gachagan A. Data-driven analysis of ultrasonic pressure tube inspection data. 2017. Paper presented at 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, United States.