Abstract
The high-speed railway (HSR) transportation system in China has been growing rapidly during the past decade. In 2016, the total length of HSR in China has reached to 22,000 kilometers, and there are over 2,000 pairs of high speed trains operating daily. With the advancement of design and manufacturing technologies, the reliability and construction costs have been improved significantly. However, there is still great need for reduction of their operation and maintenance costs. With such incentive, a pilot project has been launched to develop a prognostics and health management system for rolling stock to transform the maintenance paradigm from preventive to predictive maintenance. Considering the high task variety and big data environment in HSR real-time monitoring system, a cyberphysical system (CPS) architecture is proposed as the framework for its PHM system. This paper reviews the needs of predictive maintenance for the HSR system, and then present a concept design of the CPS-enabled smart operation and maintenance system.
Highlights
INTRODUCTIONCondition monitoring, information and communications technologies (ICT), and more importantly predictive
Introduction to Time MachineThe Cyber-Physical Interface (CPI) first introduce the concept of ‘Time Machine’ to convert the continuous and heterogeneous data source into structured data format for further computation
In 2016, there were over 2,000 pairs of high speed trains operating daily with total ridership of 1.4 billion, making the high-speed railway (HSR) China most heavily used in the world
Summary
Condition monitoring, information and communications technologies (ICT), and more importantly predictive. The high-speed rail (HSR) system in China has been growing rapidly since 2007. In 2016, there were over 2,000 pairs of high speed trains operating daily with total ridership of 1.4 billion, making the HSR China most heavily used in the world. A good example of PHM system practice in HSR is the TrainTracerTM launched by ALSTOM in 2006 for real-time remote monitoring of trains as reported by Worth et al (2014). Lu et al (2016) introduced another product named TrackTracerTM has been developed by ALSTOM that is complementary to and integrated with TrainTracerTM to further enable predictive maintenance service for track infrastructures. Predictive analytics and smart services have been introduced for enhancing the product competency and improving the sustainability of value chain. This paper discusses a framework design of predictive maintenance system based on the architecture of Cyber-Physical systems
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