Prof. Jianchun Fan Profile

Prof. Jianchun Fan

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Proceedings Article | 18 November 2024 Paper

Design and application of the standard tag set in the oil and gas pipeline domain

Quan Hui, Bing Liu, Jianchun Fan, Dehua Guo, Xiao Tan, Zeheng Peng

Proceedings Volume 13403, 1340331 (2024) https://doi.org/10.1117/12.3051764

KEYWORDS: Standards development, Industrial applications, Industry, Design, Telecommunications, Safety, Data modeling, Engineering, Data communications, Solids

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With the increasing use of digital technology across industries, standardization is undergoing a digital transformation. Machine-readable standards, characterized by their digital and structured nature, support this transformation. Standard tag sets, as specific applications of machine-readable standards, play a crucial role in creating information formats that comply with these standards, thereby improving efficiency in information processing. This study focuses on designing standard tag sets for the oil and gas pipeline domain. Through comprehensive requirements analysis, including overall and personalized needs assessment, we identify and address the challenges in developing, implementing, and maintaining these tag sets. Our research outlines the construction of both general and extended standard tag sets for oil and gas pipelines domain. The general standard tag sets mainly identify the structure of standards and technical indicators defined in standards. The extended standard tag sets achieve unification of technical element data in standards of different type by constructing domain ontology models. By proposing and implementing these standard tag sets, stored in the Neo4j database, we lay a strong foundation for knowledge organization and applications such as building oil and gas pipeline knowledge graphs. In conclusion, the standard tag sets that we put forward and design inject vitality into the digitization of the oil and gas pipeline industry, fostering innovation and development while providing essential support for its long-term progress.

Proceedings Article | 27 May 2024 Paper

Promptable model for premium connection sealing surface detection and segmentation

Shuai Yang, Jianchun Fan, Chunmeng Tian, Ting Han, Kaizhe Wei

Proceedings Volume 13169, 1316904 (2024) https://doi.org/10.1117/12.3029944

KEYWORDS: Image segmentation, Ultrasonics, Object detection, Image processing

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When applying the tubing ultrasonic testing technology to evaluate the contact condition of the metal-to-metal sealing surface of premium connection, it is necessary to judge the ultrasonic reflection amplitude image of the manual contact interface to observe whether there is sealing defect. At present, the images of phased array ultrasonic testing results usually require professionals to rely on technical knowledge to judge, the analysis has low efficiency and strong evaluation subjectivity. Therefore, there is an urgent need for an intelligent method to identify the location, range and type of sealing defects in ultrasonic images accurately and efficiently, so as assisting or replacing manual operations. Aiming at the problems of heavy reliance on data quality and inflexible segmentation effect during the process of identifying the sealing surface region using the original Mask R-CNN network, the approach described in this paper enhances the model by incorporating the Segment Anything Model(SAM) and employs prompts to guide the object detection model in generating masks that fulfill various criteria. Experiments show that the method adopted in this paper can not only correctly identify the sealing surface location, but also, compared with the original Mask R-CNN network model, it can output a segmentation mask that meets the demand according to the prompts of different segmentation criteria, and the obtained sealing surface region is closer to the theoretical segmentation region.

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