Professor Byeng Dong Youn’ Research Team Claims Winner of the 2014 PHM Data Challenge Competition Organized by the PHM Society
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SNU Mechanical and Aerospace Engineering Professor Byeng Dong Youn’s research team has been crowned the winner of the 2014 PHM Data Challenge Competition organized by one of the world’s most prestigious societies in the Prognostics and Health Management sector, the PHM Society. Professor Youn’s team was invited to the 2014 PHM Annual Conference held in Fortworth, Texas, US from the 29^th of September to the 2^nd of October to present their research.

The challenge for this year was to analyze big data from engineering systems and predict the danger levels of the system. The data was provided by GE (General Electric). The challenge was deemed difficult due to the limited nature of the data. The problem setter, Dr Dustin Garvey, said “We tried to make it as difficult as possible to allow participants to use up their full potential”. Through analysis of the given data type, the team decided on a Part Lifespan Calculation and Usage Classification method and successfully predicted the danger levels using an Ensemble approach.

The PHM Data Challenge is held every year independently by the two pain societies of PHM, the IEEE Reliability Society and the PHM Society. Professor Youn’s team managed to claim Asia’s first prize in the IEEE Reliability Society’s Data Challenge Competition in the earlier part of the year and winner of the PHM Society’s Data Challenge Competition in the later part of the year. From the fact that the US and Europe had been monopolizing all the past wins, the consecutive wins the team made has significant meaning to it by engraving the standard of national technology globally.

It is also worth nothing that the theme of this competition is the analysis of big data using the IoT, a current hot issue in the industry. While big data has been accumulating in the industry, the finding industrial worth in the data was difficult to say the least. The achievement made this year shows applicability of big data in predicting and preventing sudden faults in engineering systems. The Fault prediction diagnostics technology currently being developed by Professor Youn is a core technology of the O&M and through this we hope to add the valuable O&M to existing manufacturing processes to achieve greater progress in the creative economy.