Scholarlink

In the modern world, technology plays a pivotal role in enhancing operational efficiency across various sectors, including transportation. The railway industry, particularly focusing on sustainability and innovation, is undergoing significant changes through integration of Big Data Analytics (BDA). However, as railway utilization increases, maintaining infrastructure and equipment becomes more challenging, with tight schedules often leading to inefficiencies in traditional maintenance practices. This study addresses these challenges by implementing real-time data logging from Keretapi Tanah Melayu Berhad (KTMB) PSCADA electrification system into a Business Intelligence (BI) dashboard. KTMB, Malaysia’s leading intercity rail operator, relies on the Electrification Department to maintain its Overhead Catenary System, Power Supply Infrastructure, and SCADA System. This department’s existing maintenance reporting process is slow, paper-based, and prone to data quality defects, taking up to seven days to generate reports. To overcome these inefficiencies, the study proposes a tool developed using Microsoft Power BI, which extracts real-time data logs from the PSCADA system and presents them in a dashboard. The dashboard features interactive visualizations of key metrics like alert counts, part statuses, and fault trends, enabling the electrification team to monitor equipment performance more effectively. This innovative system allows maintenance personnel to analyze device faults based on seven key inputs (location, hourly, daily, monthly, yearly, device type, and description), and the data is displayed in corresponding graphical outputs, improving decision-making speed and accuracy. By integrating predictive maintenance strategies and reducing reporting times from seven days to just 24 h, this real-time data-driven solution enhances system reliability, aligns with Industry 4.0 principles, and reduces unnecessary waste. The System Usability Scale (SUS) validated the dashboard’s efficiency, revealing a significant 86% reduction in the time and cost associated with maintenance reporting and planning. The findings highlight the potential of BDA in optimizing railway operations and set a new benchmark for digitalized railway management in Malaysia.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Real-Time Data Monitoring Dashboard for Railway SCADA Electrification Predictive Maintenance

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Siti Nadirah Ghazali,
  • Hairulazwan Hashim,
  • Nazwa Hidayah Mohamad,
  • Nazmi Azman,
  • Amrullah Firdaus Abdul Shukor,
  • Halim Mamat,
  • Mohamad Asri Yusoff

摘要

In the modern world, technology plays a pivotal role in enhancing operational efficiency across various sectors, including transportation. The railway industry, particularly focusing on sustainability and innovation, is undergoing significant changes through integration of Big Data Analytics (BDA). However, as railway utilization increases, maintaining infrastructure and equipment becomes more challenging, with tight schedules often leading to inefficiencies in traditional maintenance practices. This study addresses these challenges by implementing real-time data logging from Keretapi Tanah Melayu Berhad (KTMB) PSCADA electrification system into a Business Intelligence (BI) dashboard. KTMB, Malaysia’s leading intercity rail operator, relies on the Electrification Department to maintain its Overhead Catenary System, Power Supply Infrastructure, and SCADA System. This department’s existing maintenance reporting process is slow, paper-based, and prone to data quality defects, taking up to seven days to generate reports. To overcome these inefficiencies, the study proposes a tool developed using Microsoft Power BI, which extracts real-time data logs from the PSCADA system and presents them in a dashboard. The dashboard features interactive visualizations of key metrics like alert counts, part statuses, and fault trends, enabling the electrification team to monitor equipment performance more effectively. This innovative system allows maintenance personnel to analyze device faults based on seven key inputs (location, hourly, daily, monthly, yearly, device type, and description), and the data is displayed in corresponding graphical outputs, improving decision-making speed and accuracy. By integrating predictive maintenance strategies and reducing reporting times from seven days to just 24 h, this real-time data-driven solution enhances system reliability, aligns with Industry 4.0 principles, and reduces unnecessary waste. The System Usability Scale (SUS) validated the dashboard’s efficiency, revealing a significant 86% reduction in the time and cost associated with maintenance reporting and planning. The findings highlight the potential of BDA in optimizing railway operations and set a new benchmark for digitalized railway management in Malaysia.