Fault Tree Simulation Analysis Source Code Based on Monte Carlo Method

Application Background

In fault tree analysis, quantitatively calculating the probability of top events is a critical component. Traditional formula-based calculations not only require substantial computational resources but also involve complex and tedious processes prone to errors. This paper introduces a novel approach using Monte Carlo simulation to model fault trees, enabling precise calculation of top event probabilities and other reliability indicators. The method features straightforward implementation through iterative random sampling algorithms, high calculation accuracy achieved through statistical convergence, and significant practical value for reliability analysis of complex systems. Code implementation typically involves event probability sampling, logical gate processing, and result aggregation modules.

Key Technologies

This paper proposes a quantitative analysis method for dynamic reliability assessment of protection systems, developed according to the functional and operational characteristics of digital relay protection systems. The methodology aims to provide references for identifying system vulnerabilities and enhancing protection design/operation reliability. The technical implementation involves three main phases: First, establishing key reliability indicators including cumulative failure probability, system availability, and basic component probability importance measures through state transition modeling. Second, constructing a dynamic reliability model for microprocessor-based protection systems by combining Markov state space with dynamic fault tree methodologies. The core algorithm implements a Monte Carlo simulation based on dynamic fault tree structure functions, featuring component state sampling, temporal logic evaluation, and reliability quantification modules. Finally, case studies validate the method's effectiveness through comparative analysis. Results demonstrate significant reference value for protection system reliability evaluation, component impact analysis, and vulnerability identification.

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In practical applications, this method enables engineers to more accurately assess protection system reliability, thereby improving system safety and stability. The simulation approach provides systematic reference data for identifying weak links in systems, facilitating optimization of system design and operation to further enhance reliability and stability. From an implementation perspective, the code structure typically includes configuration parsing, simulation engine, and results analysis components. Overall, this methodology shows broad application prospects in engineering practice and deserves further research and promotion through extended validation studies and modular code development.