Fault-Augmented Model Extension for Cyber-Physical System Failure Prediction
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Solution Overview
Problem
Current model-based diagnostic approaches lack the ability to effectively incorporate fault modes into behavioral models of cyber-physical systems, making it difficult to predict system failures and optimize design, as they primarily focus on correct behavior rather than faulty behavior.
Innovation Solution
The method involves analyzing systems to identify fault-susceptible components, augmenting their models with fault modes, simulating faults to determine system-level severity, applying physics-of-failure models to predict fault likelihood, and aggregating component degradations to predict when the system will fail to meet performance requirements, using a Fault-Augmented Model Extension (FAME) approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If behavioral models focus on correct behavior only, then model simplicity is maintained, but fault prediction capability deteriorates
Solution Approach 1:
The patent segments the behavioral model into multiple distinct fault modes, where each fault mode represents a specific failure scenario. This segmentation allows the model to handle complexity systematically by breaking down overall system behavior into manageable fault-specific components, thereby improving fault prediction capability without overwhelming model complexity
Solution Approach 2:
The patent introduces fault mode indicators as additional parameters to the behavioral model. These parameters enable the model to transition between different operational states (normal vs. various fault modes) by changing parameter values, thus enhancing fault prediction capability while maintaining a structured approach to model complexity
2Reliability
If fault modes are incorporated into behavioral models, then fault prediction capability is improved, but model complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining fault modes and their corresponding behavioral characteristics before actual fault prediction. This advance preparation organizes the complexity into structured categories, making the model more manageable while enhancing its fault prediction capability through systematic fault mode coverage
Solution Approach 2:
The patent creates a universal behavioral model framework that can handle multiple fault modes through a common structure. This multi-functionality approach allows the same model to predict various types of faults by activating different fault mode indicators, thereby improving comprehensive fault prediction capability without proportionally increasing overall model complexity
3Productivity
If design optimization is performed without fault analysis, then development time is reduced, but system reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by conducting fault mode analysis during the design stage rather than after development. This early integration of reliability analysis into the design process allows developers to identify and address potential faults before finalizing the system, thereby improving system reliability without significantly extending development time through efficient parallel processing
Data Source
AI summary
A new and/or improved method, apparatus and/or system is disclosed which aids in extending correct behavioral models to include fault modes and in fault mode analysis of components and/or systems in simulated model environments, including, e.g., FMEA and FMECA and diagnostic fault tree generation.