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Functional Reliability Evaluation for Explosive Logic Network Based on Failure Analysis

As a new application technology of burst control, the explosive logic network (ELN) and its functional realization play critical roles in the areas of weapon system (such as missiles and torpedoes) and aerospace, as well to determine the reliability and safety for initiating explosive devices. This paper investigates the failure models and mechanism of ELN from the perspective of failure analysis and reliability engineering, as well combine the method of performance design and analysis, which are of great significance to carry out its functional reliability evaluation and health status assessment accordingly. Firstly, taking the ELN with two-input-four-output into account, we discuss its failure modes and failure factors based on its structural and functional properties, then establish the fault tree model for qualitative analysis. Secondly, considering some uncertainties inherent in a certain size parameters, we calculate the reliability window of the gap null gate, in term of probabilistic model and quantitative method so as to obtain the reliability of the gap null gate. Meanwhile, referring to estimated accuracy, we introduced the H-Bayes method in the field of ELN system and initiating explosive devices. After that, the environmental monitoring data is applied to health assessment system for ELN. Moreover, when it comes to merging a variety of condition assessment information of ELN’s health level, we adopted the D-S evidential approach in information fusion for initiating explosive devices under shock conditions and electromagnetic interference. As far as the ELN is concerned, we find that: (1) Initiating explosive devices such as ELN are easily affected by environmental factors. The storage state described as storage life should be an important health indicator of ELN, which demands an effective evaluation strategy for the security and reliability considerations. (2) The functional reliability for complex ELN systems should be carried out by integrating multiple performance parameters into the evaluation method. (3) Along with three main indicators, namely, test information index, historical information index and monitoring information index, such information as polymorphism and relationship between quantitative and qualitative information should not be ignored.