Feature Extraction System during Bolt Tightening Work by Regression CNN and Classification CNN

Murai, Koichi; Imai, T.; Arai, Kohei; Kobayashi, Takao

doi:10.1109/icce50685.2021.9427750

Cited by 1 publication

(1 citation statement)

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“…Therefore, the supervised regression prediction algorithm is suitable for the research data in this paper. The deep learning algorithm of the neural network also has this wide application and a good effect on regression prediction [26,27]. Therefore, the authors use these two methods to regress the data and compare the results.…”

Section: Regression Analysismentioning

confidence: 99%

Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory

et al. 2022

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The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed.

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Section: Regression Analysismentioning

confidence: 99%