Fault detection tool for maintenance of wayside signalling and communication
The Kelana Jaya Line (KJL) is the leading urban metro train operator in Malaysia and the eldest unmanned train operation system service use the automatic train-controlled system owned by Rapid KL. The system provides the highest reliability on the signalling aspect system which equips with sensors,...
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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/10938/1/24p%20NORHAFIZA%20SAMION.pdf http://eprints.uthm.edu.my/10938/2/NORHAFIZA%20SAMION%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10938/3/NORHAFIZA%20SAMION%20WATERMARK.pdf |
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| 總結: | The Kelana Jaya Line (KJL) is the leading urban metro train operator in Malaysia and the eldest unmanned train operation system service use the automatic train-controlled system owned by Rapid KL. The system provides the highest reliability on the signalling aspect system which equips with sensors, electronics, and communication tool along the wayside. The key issue for KJL Wayside Signalling Maintenance Department team is facing a huge historical data and retrieving data failure records from the KJL Wayside Signalling data logger by manual screening method for maintenance purposes. This issue leads to time constraint and data redundancy. Therefore, this study aims to propose a tool known as a dashboard which may provide to retrieve failure data records by using Microsoft Excel software. The dashboard facilitates the team with the visualization of four selections inputs and three graphical outputs. This interactive tool made instant visibility of signalling status and assist the maintenance team to capture the trends of signalling activities. The tools begin with raw data processing using AWK programming for filtering and data cleansing for six significant variables of wayside equipment (i.e. Inductive Loop, Switch, Train, ID, Station Controller, and Platform). The result from the SUS shows the usability survey score 70.7 which is 1.04% above the global average. The study is beneficial for the organization on maintenance work in reducing time-consuming as per screening data and decision making for planning and scheduling |
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