A Study on Skytrain’s Reliability and Resilience at Singapore Changi Airport
A collaborative study by researchers from Future Resilient Systems (FRS) at the Singapore-ETH Centre and Changi Airport Group has developed a new data-driven approach to assess the Skytrain’s reliability and improve it by optimising some of its maintenance regimes.
Singapore’s Changi Airport is consistently ranked among the world's best airports, thanks in part to the seamless connectivity provided by its Skytrain system. Behind the scenes, however, maintaining the reliability and resilience of this automated people mover is a complex and critical task. A collaborative study by researchers from Future Resilient Systems (FRS) at the Singapore-ETH Centre and Changi Airport Group has developed a new data-driven approach to assess the Skytrain’s reliability and improve it by optimising some of its maintenance regimes.
Bridging Theory and Practice
The research team worked closely with Changi Airport Group, grounding their analysis in real-world operational data. Monthly performance reports, inspection records, technical drawings, and the Skytrain’s asset structure provided a rich foundation for a comprehensive reliability assessment.
A System Approach to Reliability Analysis
To systematically uncover potential vulnerabilities, the team used fault tree analysis: a structured method that traces system failures back to their root causes.
Complementing this, they built a detailed reliability block diagram to map how individual components and subsystems interact and affect overall system performance. Together, these tools provided a clear blueprint for understanding and enhancing the Skytrain’s reliability.
A New Statistical Methodology for Multiply Censored Systems Failure Data
Yet, the challenges were significant. Data on component failures was often incomplete and many components had not yet failed by the end of the study period. Additionally, spare part usage data was aggregated across vehicles, making it difficult to isolate patterns.
To overcome these hurdles, the team introduced a novel statistical method: the superposition process model. This approach allowed them to extract meaningful reliability insights from data sources, leading to more accurate assessments of component lifespans and system health.
Optimising Maintenance through the Power of Data
The findings from the study have practical implications. By identifying critical components and evaluating different maintenance strategies, the team demonstrated how maintenance schedules could be optimised. The data-driven framework enables more targeted interventions, reducing unnecessary part replacements while maintaining high operational standards.
Strengthening Airport Operations
Ultimately, the study developed a reliability evaluation framework tailored to the Skytrain system. By quantifying how different maintenance strategies impact system reliability, the framework can help support Changi Airport Group in making informed, evidence-based decisions to enhance the resilience of this vital transport link.
In an environment where even minor system failures can ripple into major flight delays and passenger dissatisfaction, maintaining the Skytrain’s resilience is mission-critical. This research contributes also offers broader insights for managing other essential transport systems worldwide.
Contributors:
Prof. Loon Ching Tang, Assoc. Prof. Zhisheng Ye, Xin Ye, Dr. Jiaxiang Cai, Wei Liu, Dr. Shen Lijuan
Further Reading:
- Li, X. Y., Ye, Z. S., & Tang, C. Y. (2021). Estimating the inter-occurrence time distribution from superposed renewal processes. Bernoulli, 27(4), 2804-2826.
- Ye, X., Cai, J., Tang, L. C., & Ye, Z. S. (2024). Statistical modeling of the effectiveness of preventive maintenance for repairable systems. Technometrics, 66(1), 118-130.