Abstract
Actual framework loads are critical to the safety of urban rail vehicles, whose operating modes have an important impact on the fatigue of framework. When the vehicle’s running states and line conditions are different, the operating modes also have obviously different characteristics. In this paper, based on the framework load-time history data of large-scale line measurement, an automatic deep learning method in data mining is used to design the program to achieve an accurate and automatic segmentation of operating conditions, thus obtaining the load-time history under different operating conditions. The calculation of the load correlations under different operating conditions is carried out, leading to corresponding correlation degree of different loads. It lays a solid foundation for building an operating mode of complex framework loads, ensuring the operational safety of rail vehicles and conducting effective reliability assessment.
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Acknowledgments
First of all, I would like to thank Professor Binjie Wang and Shouguang Sun for their guidance during the design process. In addition, I am very grateful to Mr. Wang Peng from Fudan University for his expansive explanation of relevant knowledge.
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Hu, M., Wang, B., Sun, S. (2020). Research on Framework Load Correlations Based on Automatic Data Extraction Algorithm. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2019. Advances in Intelligent Systems and Computing, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-34387-3_74
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DOI: https://doi.org/10.1007/978-3-030-34387-3_74
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