Multiple attributes integrated identification method for multiple unit train's key components

Abstract

The invention falls into the reliability field of a multiple unit train system, more particularly, a multiple attributes integrated identification method for multiple unit train's key components. The method features the following steps: firstly calculating the reliability attributes (Ri) of each component in a computer system in a successive order including the fault probability [mu]i of component i, the average mileage Mi prior to a fault, the reliability ri, and the topology attribute Ti of each component and the topology attribute comprises the node degree ki, the betweeness bi, and the closeness ci; building an attribute structure model for the components in the multiple unit train wherein the first layer is for attribute decision making, Ri and Ti constitute the second layer, and ui, Mi, ri, ki, bi and ci form the third layer; calculating the weights of each component's attributes and attribute collections. integrating the attributes in the Choquet method for the importance degree ai of each component; and ranking the ais according to their number to identify key components in a multiple unit train system. According to the invention, reliability attributes and topography attributes are well combined in a multiple unit system to identify key components in the system, which overcomes the deficiency of the prior art in considering structure consequences.

Description

technical field [0001] The invention belongs to the field of EMU system reliability, and in particular relates to a method for identifying key components of an EMU system with multi-attribute fusion. Background technique [0002] The EMU system is a complex electromechanical system with advanced functions under extreme service conditions. In recent years, on the one hand, the number of EMUs in my country has been increasing; on the other hand, domestic research on the reliability and safety of EMUs is also facing the objective fact of lack of previous experience. Identifying the key components of the EMU system, on the one hand, can optimize the structural design of the EMU vehicles and improve the reliability of the system from the source; Run at high speed. [0003] At present, there are many methods for identifying key components of EMU systems, such as Birnbaum importance, Fussell-Vesely importance, central importance, risk performance value, risk reduction value, etc....

AI Technical Summary

Problems solved by technology

[0003] At present, there are many methods for identifying key components of EMU systems, such as Birnbaum importance, Fussell-Vesely importance, central importance, risk performance value, risk reduction value, etc. in system reliability theory, but the prerequisites for these indicators The components in the system are independent of each other; in network theory, node degree, betweenness, PageRank, etc., these indicators or algorithms only consider the topological importance of the components in the system, and do not consider the attributes of the components themselves

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