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Bentley > Case Studies > SMRT Trains Leverages IoT to Enhance Rail Reliability across 282 Kilometers of Track
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SMRT Trains Leverages IoT to Enhance Rail Reliability across 282 Kilometers of Track

Technology Category
  • Functional Applications - Computerized Maintenance Management Systems (CMMS)
  • Functional Applications - Enterprise Asset Management Systems (EAM)
Applicable Industries
  • Railway & Metro
  • Transportation
Applicable Functions
  • Maintenance
Use Cases
  • Asset Health Management (AHM)
  • Asset Lifecycle Management
Services
  • System Integration
  • Training
The Challenge
SMRT Trains, the first rail operator in Singapore, operates and maintains over 282 kilometers of rail track. With an average daily ridership of over 2 million people in 2020, SMRT needed a method to keep the tracks in good condition to avoid delays and ensure reliability. They measure the system’s reliability using mean kilometers between failure (MKBF), where a failure is defined as a service delay of more than five minutes. To improve their reliability, SMRT set a target of 1 million MKBF for all their lines. However, they were relying on intensive, time-consuming, and manual maintenance planning using tens of millions of data points per year across separate data silos. They needed to upgrade their legacy processes and improve their maintenance strategy.
About The Customer
SMRT Trains Ltd is the first rail operator in Singapore, operating and maintaining over 282 kilometers of rail track since 1987. They strive to provide safe, convenient transportation services to their commuters. With an average daily ridership of over 2 million people in 2020, SMRT has a significant responsibility to ensure the reliability and safety of their rail network. They measure the system’s reliability using mean kilometers between failure (MKBF), where a failure is defined as a service delay of more than five minutes. To improve their reliability, SMRT set a target of 1 million MKBF for all their lines.
The Solution
SMRT used Bentley’s AssetWise Linear Analytics as the basis of their predictive decision support system (PDSS). The PDSS was designed to create a data repository to centralize all the data completely and accurately, as well as a data analytics engine that can analyze the data for maintenance strategy recommendation, resource optimization, and improve maintenance effectiveness. The PDSS automatically runs every day to process the data and create a prioritization table. The AssetWise-based platform automatically pulled four years of SMRT’s data from asset configuration information, inspection records, and operating data. All this information is stored in the cloud via Microsoft Azure, with new information automatically imported into the system. This automation ensures that old data is regularly updated, creating a baseline to perform maintenance. Users can then validate the information before it gets used to alert engineers of relevant work activities.
Operational Impact
  • With the implementation of the PDSS, SMRT has been able to overlay multiple data sources within seconds rather than hours. The system has significantly streamlined multiple analyses, making data easily accessible via the web. The PDSS has also enabled SMRT to optimize the efficiency of a work crew’s maximum work capacity during one shift, ensuring the safety and reliability of the rail network. Their engineers now have a better idea of the work conditions, allowing them to prepare for the repair work needed in advance to save time on the site. Moving forward, SMRT plans to continuously improve their maintenance efficiency and effectiveness.
Quantitative Benefit
  • SMRT achieved their target of 1 million MKBF across all rail lines as of August 2019.
  • Data correlation is twice as fast as the existing manual method.
  • SMRT managed to reduce hundreds of manual planning hours.

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