Model-Based Reliability Simulation for Component Degradation
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Solution Overview
Problem
Conventional system design methods rely heavily on designer experience and focus on binary failure states, neglecting the impact of gradual component failures and undesired behaviors that can lead to system instability or catastrophic failures, as they typically assume components either function correctly or fail completely, without accounting for degraded performance or interactions between components.
Innovation Solution
The development of a technical computing environment (TCE) that uses graphical and text-based modeling tools to create dynamic physical system models, allowing for the simulation of component failures and interactions, generation of reliability datasheets, and prediction of emergent failures, by incorporating reliability information from component datasheets and modeling fault trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional binary failure state assumptions are used in system design, then design simplicity is maintained, but reliability assessment accuracy deteriorates
Solution Approach 1:
The patent transitions from static binary failure states to dynamic progressive failure states. The system model now includes components that can exist in multiple states (normal, degraded, failed) with transitions between states over time, allowing reliability assessment to capture the evolving nature of component deterioration and its impact on system reliability.
Solution Approach 2:
The patent introduces new parameters to describe component health beyond binary states. Components are characterized by parameters such as degradation level, remaining useful life, and transition probabilities between states. These parameter changes enable more nuanced reliability assessment while maintaining manageable model complexity through structured parameter definitions.
2Reliability
If gradual component failures and degraded performance are modeled, then reliability assessment accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the system into discrete components, each with defined failure modes and transition probabilities. By dividing the system into manageable component units with standardized state transition models, the computational complexity is controlled while still capturing the complexity of gradual failures and degraded performance across the entire system.
Solution Approach 2:
The patent uses simulation models that replicate real system behavior without requiring physical prototypes. Virtual copies of the system and its components are created in the computational environment, allowing repeated experimentation with different failure scenarios and parameter settings without additional physical resources, thus managing computational complexity.
3Reliability
If interactions between components during failure are analyzed, then system-level reliability prediction improves, but analysis time increases
Solution Approach 1:
The patent performs preliminary analysis by pre-defining component failure modes, transition probabilities, and interaction relationships before running full system simulations. Failure mode and effects analysis (FMEA) and fault tree analysis (FTA) are conducted upfront to identify critical interaction pathways, allowing the main simulation to focus computational resources on the most significant failure scenarios and reduce overall analysis time.
Data Source
AI summary
Methods and devices for providing and using a technical computing environment (TCE) for receiving a TCE model that, when executed, simulates behavior of a dynamic physical system, and that represents one or more physical components and their respective reliability information in a block diagram model. Applications of the model include automated system-level datasheet and bill of materials generation, component reliability information discovery, fault and stress assertions, and identification of emergent faults.