铁道车辆预测性维修的机器学习模型设计

Hafid Galih Pratama Putra, S. Supangkat, I. B. Nugraha, F. Hidayat, PT Kereta
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引用次数: 4

摘要

印度尼西亚的地理布局使得创建一个具有许多分支的单一联合铁路网具有挑战性。相反,铁路运营是按地点划分的,主要是在爪哇和苏门答腊岛等大岛之间。因此,很难对每个业务领域分配适当的资源。提供铁路运输服务的Kereta Api Indonesia需要解决和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。本文将重点设计机器学习模型,使用铁路车辆(如发电机列车)的实际数据,通过使用机器学习的分类算法来预测其维护状况,这可以帮助自动进行例行检查。作为一篇初步的研究论文,机器学习模型的测试和评估尚未可用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing Machine Learning Model for Predictive Maintenance of Railway Vehicle
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.
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