DAG Bayesian Network for Dynamic Risk Mitigation in Safety-Critical Systems
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Solution Overview
Problem
Current safety-critical systems face challenges in scaling risk mitigation solutions due to varying fault requirements, leading to high overhead costs and performance gaps compared to commercial off-the-shelf high-performance computing hardware, as different faults necessitate distinct solutions that are costly in terms of hardware and software resources.
Innovation Solution
The implementation of a Directed Acyclic Graph (DAG) Bayesian network and a look-up-table (LUT) to model and mitigate risks, where the DAG network represents system states and faults with conditional probability distributions, and the LUT maps mitigation techniques to nodes, allowing dynamic activation of risk reduction methods based on runtime evidence and system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If risk mitigation techniques are implemented for all possible faults, then system safety is improved, but hardware footprint and power demand increase significantly
Solution Approach 1:
The patent applies local quality by implementing fault-specific mitigation techniques only where and when needed. The system dynamically selects and activates mitigation techniques based on the actual fault type detected, rather than uniformly applying all mitigation techniques across the entire system. This allows the system to maintain high safety standards while minimizing unnecessary power consumption and hardware overhead in regions where full mitigation is not required.
Solution Approach 2:
The system employs dynamics by enabling runtime selection and activation of mitigation techniques. The fault classification module identifies the type of fault occurring, and based on this classification, the system dynamically activates only the necessary mitigation techniques. This dynamic approach allows the system to adapt its safety measures in real-time, reducing power consumption when full mitigation is not needed while maintaining reliability when faults are detected.
2Reliability
If different mitigation techniques are used for different faults, then fault-specific effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the fault mitigation system into distinct modules: fault detection module, fault classification module, and mitigation technique selection module. Each module handles a specific aspect of the mitigation process, allowing the system to implement fault-specific techniques while maintaining manageable complexity through clear functional separation. The lookup table further segments the mitigation strategies by fault type, enabling efficient selection without requiring complex decision logic.
Solution Approach 2:
The patent introduces an intermediary lookup table that maps fault classifications to appropriate mitigation techniques. This intermediary structure simplifies the system architecture by providing a direct mapping between fault types and mitigation strategies, avoiding the need for complex real-time decision algorithms. The lookup table acts as a mediator that translates fault classification results into actionable mitigation commands, reducing overall system complexity while maintaining fault-specific effectiveness.
3Reliability
If from-the-ground-up safety processes are applied, then system safety is improved, but manufacturing cost increases
Solution Approach 1:
The patent applies universality by designing a multi-functional safety system that can handle multiple fault types using a single integrated architecture. The fault classification and dynamic mitigation selection mechanism allows the same hardware platform to provide appropriate safety measures for various fault conditions without requiring separate dedicated safety systems for each fault type. This universal approach reduces manufacturing costs by avoiding duplication while maintaining comprehensive safety coverage.
Solution Approach 2:
The system employs parameter changes by dynamically adjusting the level and type of mitigation applied based on the detected fault parameters. Rather than using a fixed high-level safety approach that increases manufacturing cost, the system modifies its safety parameters in real-time based on the actual fault conditions. This allows the system to maintain ground-up safety principles where needed while reducing costs in scenarios where full mitigation is not required, optimizing the trade-off between safety and manufacturing cost.
Data Source
AI summary
A computer-implemented method may include obtaining, from a system using a middleware component of the system, run-time evidence of the system; applying the obtained run-time evidence to a Directed Acyclic Graph (DAG) Bayesian network to determine marginal probabilities for one or more nodes of the DAG Bayesian network, wherein the DAG Bayesian network comprises a plurality of nodes each representing states and faults of the system, wherein each node includes a parameterized conditional probability distribution, and wherein one or more of the nodes of the plurality of nodes specify a list of one or more safety goals and a safety value; determining which nodes representing faults have probabilities exceeding their specified safety value; and determining one or more risk mitigation techniques to activate for the determined nodes representing faults with probabilities exceeding their respective safety value.