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2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) (2017)
Denver, CO, United States
June 26, 2017 to June 29, 2017
ISSN: 2325-6664
ISBN: 978-1-5386-2272-8
pp: 133-136
ABSTRACT
Automatic train supervision (ATS) systems are designed to improve the reliability of train services. An ATS system coordinates the trains and other systems in a metro and records alarms if faults occur. In this work, we propose a context-aware anomaly diagnosis tool to analyze the underlying causes of alarms for ATS system. Using 61-day data collected from an operational ATS system, we apply our diagnosis tool to conduct systematic analysis of the alarms and identify interesting correlations among different assets and events. Our analysis shows that the alarms can be correlated with certain system events if they are in the same operations or the assets associated with them belong to the same or linked systems. These results can improve the efficiency of anomaly diagnosis and maintenance for metro system.
INDEX TERMS
Tools, Correlation, Feature extraction, Analytical models, Context modeling, Rail transportation,log analysis, context-aware anomaly diagnosis, metro system, automatic train supervision
CITATION
Yan Li, Binbin Chen, Vincent W. Zheng, William G. Temple, Zbigniew Kalbarczyk, Yue Wu, "Enhancing Anomaly Diagnosis of Automatic Train Supervision System Based on Operation Log", 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), vol. 00, no. , pp. 133-136, 2017, doi:10.1109/DSN-W.2017.25
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