Dynamic Reliability Prediction Using Multi-Source Data Fusion
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Solution Overview
Problem
Traditional methods for dynamic reliability modeling and risk analysis of digital instrumentation and control systems in nuclear power plants face challenges due to high complexity, computational bottlenecks, and difficulty in expanding to large complex systems, especially with strong interaction dynamics and high uncertainty.
Innovation Solution
A multi-source data fusion method and system for dynamic system scenario behavior deduction and reliability prediction analysis, which involves particle swarm distribution, data assimilation, and self-updating construction of a system state transition probability mapping matrix model to improve modeling accuracy and efficiency, using Gaussian sampling, Monte Carlo simulation, and Markov/CCMT models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static fault tree/event tree analysis methods are used, then the analysis process is simple, but they cannot effectively achieve dynamic reliability modeling and risk analysis of digital instrumentation and control systems
Solution Approach 1:
The patent transitions from static fault tree/event tree analysis to dynamic reliability modeling using Markov/CCMT models and dynamic flowgraph methodology. The system models time-varying failure rates and dynamic system states, enabling accurate representation of digital control system behavior under changing conditions while managing complexity through structured mathematical frameworks.
Solution Approach 2:
The patent introduces time-dependent parameters and state variables to transform static reliability models into dynamic ones. By incorporating time-varying failure rates, repair rates, and system state transitions, the model captures the evolving reliability characteristics of digital instrumentation and control systems without requiring overly complex analytical structures.
2Reliability
If new dynamic reliability prediction analysis methods are used, then dynamic reliability modeling capability is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the complex digital control system into modular functional units and subsystems, each with defined failure modes and transition probabilities. This segmentation allows the overall reliability analysis to be performed through composition of smaller, computationally manageable modules, reducing the burden of calculating system-wide dynamic reliability for large complex systems.
Solution Approach 2:
The patent applies different levels of modeling detail to different system components based on their criticality and complexity. Critical subsystems receive more detailed Markov/CCMT analysis while less critical components use simplified models, optimizing computational resources while maintaining overall prediction accuracy for the complete system.
3Measurement precision
If comprehensive dynamic analysis of large complex systems is performed, then modeling accuracy is improved, but the computational burden becomes unmanageable
Solution Approach 1:
The patent performs preliminary system decomposition, state space definition, and transition probability calculation before conducting full dynamic reliability analysis. By pre-processing the system model, identifying critical paths, and preparing conditional probability tables in advance, the computational workload during actual reliability prediction is significantly reduced while maintaining comprehensive modeling accuracy.
Data Source
AI summary
The present invention discloses a multi-source data fusion method and system for dynamic system scenario behavior deduction and reliability prediction analysis, a computer device, and a storage medium. Based on a Markov/CCMT dynamic reliability prediction analysis method and combined with a multi-source data fusion and assimilation method, the method simulates and statistically analyzes complex dynamic behavior characteristics of digital process control with strong interactive coupling, nonlinearity and high uncertainty by Monte Carlo probability model random sampling, and then achieves forward deduction analysis and reliability prediction of a system operation state through dynamic search analysis of a system state transition probability matrix model.