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The overhead contact system (OCS), as the power source of electrified railway, has a complex composition and various types of faults, so it places high requirements on its fault prevention. In recent years, with the establishment of railway OCS fault database, association analysis has been used to implement fault prevention from system-wise perspective and provide guidance for operation and maintenance. However, due to the hierarchical structure of fault database, the existing frequent itemset mining has a lot of redundancy in the results, and cannot locate the most precise faults, which affects the decision-making and makes troubleshooting lack of pertinence. To address this issue, this paper proposed a new concept, called marginal frequent itemset, which is an itemset composed of as precise items as possible in hierarchical database that meets the threshold, and an alternative mining task: mining marginal frequent itemsets instead of all the frequent itemsets. Two methods, path transform and descending depth of itemset, are proposed for achieving mining a set of marginal frequent itemsets. Two novel measures, margin degree and marginal information quantity, are proposed to evaluate the content of the mining results. An efficient algorithm, named MFIM CL , is developed for mining cross-level marginal frequent itemsets from railway OCS fault database. Our performance study shows that MFIM CL has high performance and can obtain more key information and reduce the number of results. Furthermore, marginal frequent itemset mining can simplify the fault relation network constructed by association rules and optimize the decision-making process for fault prevention of railway OCS. • Marginal frequent itemset can locate the most precise items in the database with hierarchical structure in association analysis. • Margin degree and marginal information quantity are designed to evaluate the content of mining results. • The algorithm MFIM CL outperforms existent algorithms for railway fault and can reduce the redundancy of results. • The decision-making for maintenance based on fault relation network can be optimized by marginal frequent itemset mining. [ABSTRACT FROM AUTHOR]

• A two-layer energy management strategy based on fuzzy control for high-speed railway hybrid energy storage system is proposed. • Discharge threshold can be adaptively adjusted with power and lifespan of hybrid energy storage system. • The energy management system intelligently allocates power according to the state of charge of each subsystem and the traction power. • This strategy reduces the average energy extraction power and extends the life of the energy storage system. In order to extend the service life of the high-speed railway hybrid energy storage system and reduce the power shock impact of the traction network, an energy management strategy based on double-layer fuzzy logic control is proposed. This strategy can dynamically adjust the discharge threshold according to the external power and the remaining life of the energy storage system, and dynamically allocate power according to the real-time state of charge. At the same time, the strategy uses the rain flow counting method to complete the extraction of the depth of discharge and establishes an equivalent life evaluation model. The simulation results show that this energy management strategy can effectively reduce the frequency and depth of charging and discharging, and reduce the load impact of the traction network. Compared with the threshold-based energy management strategy, the life loss of this method can be reduced by more than 50%, which effectively improves the service life of the energy storage system. [ABSTRACT FROM AUTHOR]