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Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

Author

Listed:
  • Yong-Jae Lee

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea)

  • Young Jae Han

    (Railroad Type Approval Team, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang 16105, Republic of Korea)

  • Sang-Soo Kim

    (High-Speed Railroad Research Department, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang 16105, Republic of Korea)

  • Chulung Lee

    (School of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

Abstract

The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

Suggested Citation

  • Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:278-:d:1013629
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    References listed on IDEAS

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