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Detection of coal wagon load distributions based on geometrical features using extreme learning machine methods

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Abstract

This paper proposes a new method to predict the unbalanced load distribution at coal-loaded wagons based on geometrical features by utilizing extreme learning machines. The proposed method is varied at various hyperparameter values and activation functions, also compared with back propagation artificial neural networks using various activation functions. The proposed inputs of the model are the geometrical features extracted from load shape with the pre-determined rule. The model’s output is the load value for each bogie by considering transverse and longitudinal unbalanced load conditions. For developing the model, the training data is obtained from finite element simulation by defining the coal geometries and the weights. The simulation is based on a coal wagon with a 50-Ton capacity. The proposed machine learning model has been evaluated and shows a good agreement between the prediction and the modeling data. Then, the predicted load/stress values can be utilized to assess whether the safety condition is disregarded.

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The data that support the findings of this study are available upon reasonable request from the authors.

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Acknowledgements

This work is funded by Direktorat Jenderal Pendidikan Vokasi, Ministry of Education, Culture, Research, and Technology, Indonesia, through the P2V program with contract number 15/D4/RA.02.02/2022 and 059/SPK/D4/PPK.01.APTV/VI/2022.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia

    Ignatius Anagra, Irfan Bahiuddin, Agustinus Winarno, Suryo Darmo & Rienetta Ichmawati Delia Sandhy

  2. Mechanical Engineering Department, Faculty of Industrial Technology, Universitas Trisakti, Jalan Kyai Tapa No.1 Grogol , 11440, Jakarta Barat, Indonesia

    Herjuno Rizki Priatomo

  3. Engineering Materials and Structures (eMast) iKohza, Malaysian-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

    Saiful Amri Mazlan

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  1. Ignatius Anagra
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Contributions

Writing—original draft, IA, RIDS, IB; supervision, IB; review and revision, AW, SAM, IB; methodology, IA, SD, IB; software, IA, IB; data curation, AW, HRP. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Irfan Bahiuddin.

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Anagra, I., Bahiuddin, I., Priatomo, H.R. et al. Detection of coal wagon load distributions based on geometrical features using extreme learning machine methods. Int. j. inf. tecnol. 16, 939–947 (2024). https://doi.org/10.1007/s41870-023-01499-x

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