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Automated Contingency Management or ACM can be considered the ultimate technological goal of a health management system. The ability to confidently and autonomously adapt to fault conditions with the goal of still achieving mission objectives is a significant technical challenge that is dependent on the proper performance of several supporting technologies as well as the ACM system itself. These technologies include sensors and anomaly detection, diagnostics/prognostic algorithms (PHM), reasoning algorithms, and of course, the control software and hardware required to carryout ACM. The Automated Contingency Management (ACM) system provides a framework to accommodate these technologies and leads to the design of high confidence propulsion systems with robust fault accommodation and adaptive engine operation reconfiguration necessary for the next generation aero propulsion systems. The ACM technology in general performs a multi-objective constrained optimization to accommodate impending failure conditions, and provides such potential benefits as:

  • Improved probability of mission completion
  • Increased safety of operations
  • Technically accurate contingencies (e.g. engine will recover from surge event; does not shutdown)
  • Reduced cost of operations
  • Optimized maintenance intervals and prioritization of tasks to be performed during the planned maintenance interval
  • Reduced design safety margin generally equating to improved performance
  • Reduced human burden due to increased autonomy


We define the ACM problem in the following manner:

Given the current state of the system and subject to predefined system constrains, find the optimal action series that will bring the system to the desired state with a minimal cost and a highest probability of success.

Diagram of ACM

An ACM system should be expected to bring the system to a degraded nominal space if reconfiguration is feasible else it should switch to a Fail-Safe mode to guarantee safety and success of the mission.


International Space Station (ISS) - The ISS power management system can be considered a rudimentary and manual version of contingency management that requires manual labor and involves much slower control loops. ACM can help reduce the need for manual labor and improve the maintainability and reliability of the ISS power systems.

Unmanned Autonomous Vehicles (UAVs) - UAVs have specific requirements for fully automated embedded diagnostics and prognostics. Health management is a design-in feature for UAVs and faults are handled through automated contingency management methods.

Joint Strike Fighter (JSF) - One of the overall goals of the JSF project is to reduce development, production, and ownership costs for the next-generation fighter aircraft. Fundamental to the success of this program are the development of on-line prognostics and health management (PHM) and Autonomic Logistics for the aircraft systems.

Space Shuttle Guidance Navigation & Control System (STS GN&C) - The GN&C system on a space shuttle includes several modules that help to recover from contingencies especially during ascent and reentry. For instance, it employs 44 jet thrusters as a part of its reaction control system that maneuvers the shuttle during reentry to provide even heat distribution to the heat shield. An ACM system integrated with prognostic information based on sensor data is expected to yield more robust and reliable mechanisms to ensure shuttle safety.

Several NASA Applications that can benefit from ACM
Several potential NASA/DoD related applications that can benefit from ACM


  • Increased confidence for scheduling propulsion module removals and fault diagnosis
  • Improved safety associated with operating and maintaining propulsion systems
  • Reduced life cycle costs of propulsion from installation to retirement
  • Optimized maintenance intervals for specific propulsion components and prioritization of tasks to be performed during the planned maintenance interval
  • Increased up-time/availability of all propulsion within a fleet


  • Demonstrate hierarchical decision making for reconfiguration based on prognostic information available to an ACM system
  • Mature the capability of the ACM Test Bench as a prototyping environment for PHM/ACM algorithm development
  • Develop specific ACM methods and a reconfigurable control module for propulsion-related systems
  • Demonstrate the generic capabilities of the ACM Test Bench and specific performance of the PHM and ACM modules

*This work was performed in collaboration with Impact Technologies, LLC and Georgia Tech under the SBIR Contract No NNA05AC53C.

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