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Designing Machine Learning Model for Predictive Maintenance of Railway Vehicle

机译:铁路车辆预测性维修的机器学习模型设计

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Indonesia’s geographical layout makes it challenging to create a single united railroad network with many branches. Instead, the railroad operation is divided by location, mainly between large islands such as Java and Sumatra. Therefore, distributing appropriate resources for each operational area is hard to manage properly. Kereta Api Indonesia, which provides rail transportation services, needs to address and change its various business processes to achieve Digital Transformation. Among them, asset maintenance is crucial in railroad operations. This paper will focus on designing machine learning models using actual data available from railway vehicles such as generator trains to predict their condition for maintenance by utilizing classifying algorithms for machine learning, which can help automate routine checks. As a preliminary research paper, testing and evaluation of the machine learning model are not available yet.
机译:印度尼西亚的地理布局使得创建一个由多个分支机构组成的统一铁路网具有挑战性。相反,铁路运营按位置划分,主要在爪哇岛和苏门答腊岛等大型岛屿之间。因此,为每个作战区域分配适当的资源是很难妥善管理的。提供铁路运输服务的Kereta Api Indonesia需要解决并改变其各种业务流程,以实现数字化转型。其中,资产维护在铁路运营中至关重要。本文将着重于利用铁路车辆(如发电机组)的实际数据设计机器学习模型,通过使用机器学习的分类算法来预测其维修状况,这有助于实现例行检查的自动化。作为一篇初步的研究论文,目前还没有对机器学习模型进行测试和评估。

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