• Title/Summary/Keyword: fault detect

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Study on Signal Processing in Eddy Current Testing for Defects in Spline Gear (스플라인 기어부 결함의 와전류검사 신호처리에 관한 연구)

  • Lee, Jae Ho;Park, Tae Sung;Park, Ik Keun
    • Journal of the Korean Society for Nondestructive Testing
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    • v.36 no.3
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    • pp.195-201
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    • 2016
  • Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

A Development of Welding Information Management and Defect Inspection Platform based on Artificial Intelligent for Shipbuilding and Maritime Industry (인공지능 기반 조선해양 용접 품질 정보 관리 및 결함 검사 플랫폼 개발)

  • Hwang, Hun-Gyu;Kim, Bae-Sung;Woo, Yun-Tae;Yoon, Young-Wook;Shin, Sung-chul;Oh, Sang-jin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.2
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    • pp.193-201
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    • 2021
  • The welding has a high proportion of the production and drying of ships or offshore plants. Non-destructive testing is carried out to verify the quality of welds in Korea, radiography test (RT) is mainly used. Currently, most shipyards adopt analog-type techniques to print the films through the shoot of welding parts. Therefore, the time required from radiography test to pass or fail judgment is long and complex, and is being manually carried out by qualified inspectors. To improve this problem, this paper covers a platform for scanning and digitalizing RT films occurring in shipyards with high resolution, accumulating them in management servers, and applying artificial intelligence (AI) technology to detect welding defects. To do this, we describe the process of designing and developing RT film scanning equipment, welding inspection information integrated management platform, fault reading algorithms, visualization software, and testing and verification of each developed element in conjunction.