EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES

EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES

In this information age, the use of computers and various digital devices is familiar in everyday life. These devices can assist in human work by processing information and data quickly. Thus, the application of these devices in the industry can increase productivity. In Indonesia, several sectors a...

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Main Author: Galih Pratama Putra, Hafid
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/62338
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:62338
spelling id-itb.:623382021-12-27T11:52:54ZEVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES Galih Pratama Putra, Hafid Indonesia Theses Predictive maintenance, trains, machine learning. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/62338 In this information age, the use of computers and various digital devices is familiar in everyday life. These devices can assist in human work by processing information and data quickly. Thus, the application of these devices in the industry can increase productivity. In Indonesia, several sectors are trying to adapt and implement digitalization in their business processes. One of these sectors is the transportation industry, where now many transportation systems utilize the latest technology in their business processes. Here is the opportunity for PT Kereta Api Indonesia to carry out the Digital Transformation. Among the various business processes that can be digitized, asset maintenance or maintenance is essential in railroad operations. This thesis will discuss the potential of predictive maintenance can be useful in ensuring that trains vehicles are in good condition by utilizing the artificial intelligence method of machine learning, which can help automate routine checks by classifying its condition from the on-board sensors. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description In this information age, the use of computers and various digital devices is familiar in everyday life. These devices can assist in human work by processing information and data quickly. Thus, the application of these devices in the industry can increase productivity. In Indonesia, several sectors are trying to adapt and implement digitalization in their business processes. One of these sectors is the transportation industry, where now many transportation systems utilize the latest technology in their business processes. Here is the opportunity for PT Kereta Api Indonesia to carry out the Digital Transformation. Among the various business processes that can be digitized, asset maintenance or maintenance is essential in railroad operations. This thesis will discuss the potential of predictive maintenance can be useful in ensuring that trains vehicles are in good condition by utilizing the artificial intelligence method of machine learning, which can help automate routine checks by classifying its condition from the on-board sensors.
format Theses
author Galih Pratama Putra, Hafid
spellingShingle Galih Pratama Putra, Hafid
EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
author_facet Galih Pratama Putra, Hafid
author_sort Galih Pratama Putra, Hafid
title EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
title_short EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
title_full EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
title_fullStr EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
title_full_unstemmed EVALUATION OF MACHINE LEARNING USE FOR PREDICTIVE MAINTENANCE OF RAILWAY VEHICLES
title_sort evaluation of machine learning use for predictive maintenance of railway vehicles
url https://digilib.itb.ac.id/gdl/view/62338
_version_ 1822931907427434496

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