Search results

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

This paper aims to address the pressing challenges in research data management within institutional repositories, focusing on the escalating volume, heterogeneity and multi-source nature of research data. The aim is to enhance the data services provided by institutional repositories and modernise their role in the research ecosystem.

The authors analyse the evolution of data management architectures through literature review, emphasising the advantages of data lakehouses. Using the design science research methodology, the authors develop an end-to-end data lakehouse architecture tailored to the needs of institutional repositories. This design is refined through interviews with data management professionals, institutional repository administrators and researchers.

The authors present a comprehensive framework for data lakehouse architecture, comprising five fundamental layers: data collection, data storage, data processing, data management and data services. Each layer articulates the implementation steps, delineates the dependencies between them and identifies potential obstacles with corresponding mitigation strategies.

The proposed data lakehouse architecture provides a practical and scalable solution for institutional repositories to manage research data. It offers a range of benefits, including enhanced data management capabilities, expanded data services, improved researcher experience and a modernised institutional repository ecosystem. The paper also identifies and addresses potential implementation obstacles and provides valuable guidance for institutions embarking on the adoption of this architecture. The implementation in a university library showcases how the architecture enhances data sharing among researchers and empowers institutional repository administrators with comprehensive oversight and control of the university’s research data landscape.

This paper enriches the theoretical knowledge and provides a comprehensive research framework and paradigm for scholars in research data management. It details a pioneering application of the data lakehouse architecture in an academic setting, highlighting its practical benefits and adaptability to meet the specific needs of institutional repositories.