TY - GEN
T1 - Smart Maintenance via Dynamic Fault Tree Analysis
T2 - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017
AU - Liu, Yan
AU - Wu, Yue
AU - Kalbarczyk, Zbigniew
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/30
Y1 - 2017/8/30
N2 - Urban railway systems, as the most heavily used systems in daily life, suffer from frequent service disruptions resulting millions of affected passengers and huge economic losses. Maintenance of the systems is done by maintaining individual devices in fixed cycles. It is time consuming, yet not effective. Thus, to reduce service failures through smart maintenance is becoming one of the top priorities of the system operators. In this paper, we propose a data driven approach that is to decide maintenance cycle based on estimating the mean time to failure of the system. There are two challenges: 1) as a cyber physical system, hardwares of cyber components (like signalling devices) fail more frequently than physical components (like power plants), 2) as a system of systems, functional dependency exists not only between components within a sub-system but also between different sub-systems, for example, a train relies on traction power system to operate. To meet the challenges, a Dynamic Fault Tree (DFT) based approach is adopted for the expressiveness of the modelling formalism and an efficient tool support by DFTCalc. Our case study shows interesting results that the Singapore Massive Rapid Train (MRT) system is likely to fail in 20 days from the full functioning status based on the manufacture data.
AB - Urban railway systems, as the most heavily used systems in daily life, suffer from frequent service disruptions resulting millions of affected passengers and huge economic losses. Maintenance of the systems is done by maintaining individual devices in fixed cycles. It is time consuming, yet not effective. Thus, to reduce service failures through smart maintenance is becoming one of the top priorities of the system operators. In this paper, we propose a data driven approach that is to decide maintenance cycle based on estimating the mean time to failure of the system. There are two challenges: 1) as a cyber physical system, hardwares of cyber components (like signalling devices) fail more frequently than physical components (like power plants), 2) as a system of systems, functional dependency exists not only between components within a sub-system but also between different sub-systems, for example, a train relies on traction power system to operate. To meet the challenges, a Dynamic Fault Tree (DFT) based approach is adopted for the expressiveness of the modelling formalism and an efficient tool support by DFTCalc. Our case study shows interesting results that the Singapore Massive Rapid Train (MRT) system is likely to fail in 20 days from the full functioning status based on the manufacture data.
KW - Critical Infrastructure
KW - Dynamic Fault Tree Analysis
KW - Smart Maintenance
KW - Urban Railway System
UR - http://www.scopus.com/inward/record.url?scp=85031685001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031685001&partnerID=8YFLogxK
U2 - 10.1109/DSN.2017.50
DO - 10.1109/DSN.2017.50
M3 - Conference contribution
AN - SCOPUS:85031685001
T3 - Proceedings - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017
SP - 511
EP - 518
BT - Proceedings - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2017 through 29 June 2017
ER -