Qualitative fault modeling in safety critical Cyber Physical Systems

Ajay Chhokra, Nagabhushan Mahadevan, Abhishek Dubey, Gabor Karsa

12th System Analysis and Modelling Conference 2020

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Why This Matters

Autonomous systems must operate in complex, dynamic environments where the probability and severity of hazards change based on system state and environmental conditions. Static design-time risk assessments cannot capture these dynamic variations, limiting the ability of systems to maintain safety during continuous operation. This work is innovative because it bridges safety assurance methodologies with runtime operations, enabling systems to estimate risk dynamically and support adaptive safety management based on current operational conditions.

What We Did

This paper introduces ReSONAte, a dynamic runtime risk assessment framework for autonomous cyber-physical systems that uses Bow-Tie Diagrams to model hazard propagation and estimate risk based on current system state. The framework integrates design-time hazard analysis with runtime monitoring and state-dependent risk calculations. The methodology enables autonomous systems to dynamically adjust their operations based on estimated risk values in response to changing environmental conditions.

Key Results

The ReSONAte framework successfully demonstrates dynamic risk estimation for autonomous vehicles in simulation environments. The approach shows that risk calculations can be performed efficiently at runtime while accurately reflecting state-dependent hazard probabilities. Results demonstrate that the framework enables autonomous systems to estimate risk based on current conditions and support dynamic decision-making that maintains safety during continuous operation.

Full Abstract

Cite This Paper

@inproceedings{chhokrasam2020,
  author = {Chhokra, Ajay and Mahadevan, Nagabhushan and Dubey, Abhishek and Karsa, Gabor},
  booktitle = {12th System Analysis and Modelling Conference},
  title = {Qualitative fault modeling in safety critical Cyber Physical Systems},
  year = {2020},
  abstract = {One of the key requirements for designing safety critical cyber physical systems (CPS) is to ensure resiliency. Typically, the cyber sub-system in a CPS is empowered with protection devices that quickly detect and isolate faulty components to avoid failures. However, these protection devices can have internal faults that can cause cascading failures, leading to system collapse. Thus, to guarantee the resiliency of the system, it is necessary to identifythe root cause(s) of a given system disturbance to take appropriate control actions. Correct failure diagnosis in such systems depends upon an integrated fault model of the system that captures the effect of faults in CPS as well as nominal and faulty operation of protection devices, sensors, and actuators. In this paper, we propose a novel graph based qualitative fault modeling formalism for CPS, called, Temporal Causal Diagrams(TCDs) that allow system designers to effectively represent faultsand their effects in both physical and cyber sub-systems. The paper also discusses in detail the fault propagation and execution semantics of a TCD model by translating to timed automata and thus allowing an efficient means to quickly analyze, validate and verify the fault model. In the end, we show the efficacy of the modeling approach with the help of a case study from energy system. },
  contribution = {minor},
  tag = {platform},
  keywords = {autonomous systems, runtime risk assessment, safety assurance, Bow-Tie Diagrams, cyber-physical systems, state-dependent risk}
}
Quick Info
Year 2020
Keywords
autonomous systems runtime risk assessment safety assurance Bow-Tie Diagrams cyber-physical systems state-dependent risk
Research Areas
CPS Explainable AI scalable AI
Search Tags

Qualitative, fault, modeling, safety, critical, Cyber, Physical, Systems, autonomous systems, runtime risk assessment, safety assurance, Bow-Tie Diagrams, cyber-physical systems, state-dependent risk, CPS, Explainable AI, scalable AI, 2020, Chhokra, Mahadevan, Dubey, Karsa