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As a versatile energy carrier, hydrogen possesses tremendous potential to reduce greenhouse emissions and promote energy transition. Global interest in producing hydrogen from renewable energy sources and transporting, storing, and utilizing hydrogen is rising rapidly. However, the high costs of producing clean hydrogen and the uncertain application scenarios for hydrogen energy result in its relatively limited utilization worldwide. It is necessary to find new promising technological paths to drive the development of hydrogen energy. As part of technological innovation, emerging technologies have vital features such as prominent impact, novelty, relatively fast growth, etc. Identifying emerging hydrogen-energy-related technologies is important for discovering innovation opportunities during the energy transition. Existing research lacks analysis of the characteristics of emerging technologies. Thus, this paper proposes a method combining the latent Dirichlet allocation topic model and hydrogen-energy expert group decision-making. This is used to identify emerging hydrogen-related technology regarding two features of emerging technologies, novelty and prominent impact. After data processing, topic modeling, and analysis, the patent dataset was divided into twenty topics. Six emerging topics possess novelty and prominent impact among twenty topics. The results show that the current hotspots aim to promote the application of hydrogen energy by improving the performance of production catalysts, overcoming the wide power fluctuations and large-scale instability of renewable energy power generation, and developing advanced hydrogen safety technologies. This method efficiently identifies emerging technologies from patents and studies their development trends. It fills a gap in the research on emerging technologies in hydrogen-related energy. Research achievements could support the selection of technology pathways during the low-carbon energy transition. [ABSTRACT FROM AUTHOR]

Gear tooth spalling is one of the inevitable fault modes in the long-term service of the traction transmission system of railway vehicles, which can worsen the dynamic load of the rotating mechanical system and reduce the operating quality. Therefore, it is necessary to study its fault mechanism to guide fault diagnosis scientifically. This paper established a planar railway vehicle model with a traction transmission system and an analytical time-varying meshing stiffness (TVMS) model of the spalling spur gear. Then, it analyzed the dynamic characteristics under traction conditions. The research found that the spalling length and depth affect the amplitude of the TVMS at the defect, while the width affects the range of the TVMS loss. The crest factor is the best evaluation indicator in ideal low-noise environments due to its sensitivity and linearity, but it is not good in strong-noise environments. Similarly, a time–frequency analysis tool cannot significantly detect the sideband characteristics that are excited by spalling. After high-pass filtering, the root mean square and variance exhibit excellent classification and vehicle speed independence in strong-noise environments. This research achievement can provide adequate theoretical support for feature selection and making strategies for fault diagnosis of railway vehicle gear systems. [ABSTRACT FROM AUTHOR]