The application of dynamic bayesian network to reliability assessment of emu traction system

In recent years, a new era has seen the development of high-speed railway in China. By the end of 2012, China has boasted the coverage of about 9,356-km-high-speed railway[11]. The Beijing-Shanghai High-speed Railway which began to operate in July, 2011 has further pushed China towards super-high-speed trains with an operating speed of 380 km/h [20]. During the 13th five-year plan, the high-speed railway is supposed to increase to 30,000km, covering more than 80% of big cities. This widespread coverage has definitely rendered the reliability of EMUs a top priority. Nowadays, EMUs are generally ascribed to the extreme complexity and interdependencies as a result of the systematic use of new technologies (such as artificial intelligence, information/communication technologies, or communication networks). Failures of EMUs could cause a catastrophic accident, for example, the Wenzhou High-speed train crash on July 23, 2011. To sum up, the extreme reliability, the most critical of EMUs regarding the traction system, can never be underestimated. Over the past decade, the need to conduct an analysis of systematic reliability and safety assessment with respect to EMUs has long been recognized. In an effort to avoid economic losses and heavy casualties arising from safety violations, a large number of studies have been conducted to combine risk-based reliability analysis into safety control of EMUs. For example, Hanmin Lee, EuijinJoung, et al [18] built the management system in PDM (Product Data management) for failure history data to analyze the reliability of advanced EMU. Joung, E.[14], on the basis of the referenced RAMS standards, presented a system of reliability prediction and relevant demonstration procedure Yanhui WAng Lifeng Bi Shujun WAng Shuai Lin Wanxiao XiAng