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Mean Kilometres Between Failures (MKBF) - Coggle Diagram
Mean Kilometres Between Failures (MKBF)
Formula:
total train kilometers traveled / number of failures
higher MKBF signifies a more reliable system with fewer delays
Before
manual logging of major service disruptions
calculation with manual data input
After
integrating of AI which use advanced data analytic
Leveraging advanced technology
adopting of international standards
effective incident management practice
International Standard: SS ISO 55001
Project Overwatch which uses AI to detects train anomalies in real time
managing assets, minimizing risks, and enhancing service
effective incident management practices
ensure that a rapid respond team can quickly resolved any disruption incident, minimised the impact of failures and reduce delay period.
reviewing and analysing helps to identify the cause of the incident. Doing so helps in implementing a preventive measure to reduce the chance of the same incident to occur again
Leveraging advanced technology: IoT sensors
SMRT using Bentley’s AssetWise Linear Analytics
Difference?
Identifies potential issues before they occur, allowing for proactive maintenance actions.
identify potential failures in asset performance and implement ways to reduce the risks
provides predictive analytics which uses machine learning algorithms to predict when and where failures are likely to occur,
prioritise maintenance more on critical areas that have the highest impact on service reliability.
As of 2024, among the 5 lines:
average of 2.32 million train-km MKBF
Downtown Line was the most reliable ( 8.15 million train-km)
Circle Line was the least reliable ( 5.05 million train-km)
Thomson-East Coast Line is not included as it is not finish
Year 2015,
significant increase in service disruptions and breakdowns
Khaw Boon Wan took over the role of as the Minister for Transport
average 133,000 train-km MKBF of all 5 lines
Year 2017.
Khaw Boon Wan aimed to bring Singapore’s RTS system to be same level or even better than other world-class metro systems such as those in Hong Kong and Taipei
Year 2019,
rose to over average 1 million train-km MKBF of all 5 lines
Year 2020,
Khaw Boon Wan retirement
1.6 million train-km MKBF for the period ended June
Khaw Boon Wan's Contributions:
Focus on maintenance
Enhancements to the signalling system
Enhancing workforce
preventive maintenance and condition-based maintenance
Preventive:
identifying any potential issues before they escalate such as signalling, communication system, power supply, infrastructure and track conditions.
use of sweep train to inspect RTS
uses Internet of Things (IoT) to provides real-time data from various sensors placed along the tracks and on trains. This will detect any failures such as cable insulator or any critical asset failure.
Advance condition monitoring
improving the skills and capability of the workforce
improve regular training
following and understanding the latest technologies, so that they can efficiency handle maintaining the RTS system.
Upgrading signalling system and replacing ageing trains helps to improve train frequency and reliability
New signalling system will allow trains to have a closer headway while maintaining a safe distance, reducing inverter time between trains
Replacing old with new trains equipped with the latest technology making them less likely to breakdowns and cause delays.