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Area of Science:

  • Reliability Engineering
  • Operations Research
  • System Safety

Background:

  • Safety-critical systems face degradation risks impacting mission success.
  • Component failure in these systems can lead to catastrophic safety losses.
  • Limited spare parts and mission aborts complicate risk management.

Purpose of the Study:

  • To develop adaptive strategies for component replacement and mission termination.
  • To optimize the balance between mission completion and system survivability.
  • To address risk management challenges in safety-critical systems with resource limitations.

Main Methods:

  • Modeling component degradation using a continuous-time discrete-state Markov chain.
  • Formulating the joint decision problem as a finite-time Markov decision process.
  • Analyzing structural properties and establishing control-limit policies for replacements and mission termination.

Main Results:

  • Developed structured control-limit policies for adaptive spare replacement and mission termination.
  • Validated superior model performance in enhancing operational efficiency and mitigating risks.
  • Demonstrated effectiveness through numerical experiments on a radar system driver.

Conclusions:

  • The proposed adaptive policies effectively manage risks in safety-critical systems.
  • Optimized decision-making enhances system survivability and mission success rates.
  • The approach provides a robust framework for resource-limited operational environments.