As a part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principles which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems 123 . The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of IIVM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and 1 0-7803-8870-4/05/$20.00© 2005 IEEE 2 IEEEAC paper #1204, Version 2, mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for IIVM includes: 1) enhanced communications and telemetry, 2) sensors for radiation materials, 3) vehicle controls and dynamics, 4) flight mechanics and control, 4) embedded sensors for structural integrity of tanking systems, 5) evolutionary concepts for embedded sensor placement in tank systems, 6) real time operating systems, and 7) computer architectures for distribute...
The use of soft-computing algorithms in hardware-in-the-loop applications has been investigated. A Proportional-Integral-Derivative (PID) classical controller and a Fuzzy Logic Controller (FLC) were designed and successfully tested on the Turbine Technologies SR-30 turbojet engine for the main-stage operation of the engine. Transfer function model of the plant was obtained using frequency-response method. Closed-loop controllers both with the PID and FLC algorithms were tested in a simulated environment before their application. Additionally, application of Bayesian Belief Networks (BBN) for the start-up sequence of the engine was investigated and a BBN design has been made.
Human exploration of the solar system requires fully autonomous systems when travelling more than 5 light minutes from Earth. This autonomy is necessary to manage a large, complex spacecraft with limited crew members and skills available. The communication latency requires the vehicle to deal with events with only limited crew interaction in most cases. The engineering of these systems requires an extensive knowledge of the spacecraft systems, information theory, and autonomous algorithm characteristics. The characteristics of the spacecraft systems must be matched with the autonomous algorithm characteristics to reliably monitor and control the system. This presents a large system engineering problem. Recent work on product-focused, elegant system engineering will be applied to this application, looking at the full autonomy stack, the matching of autonomous systems to spacecraft systems, and the integration of different types of algorithms. Each of these areas will be outlined and a general approach defined for system engineering to provide the optimal solution to the given application context.
The success of NASA's exploration activities hinges on the ability to make space systems safer, more affordable, and more self-sufficient. As these missions expand to ever increasing distances from earth, the systems that support these missions will be required to become more self-sufficient. Integrated Vehicle Health Management (IVHM) is an approach that supports vehicle selfsufficiency. The architecture presented in this paper integrates advanced computational techniques with technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). This paper presents an Integrated Intelligent Vehicle Management (IIVM) concept which provides for the complete integration and management of all vehicle functions and subsystems 12This research presents architecture for vehicle level interaction between subsystem functions and vehicle level functions. This paper presents an IIVM framework that encompasses all vehicle functions and subsystems. Furthermore, this paper presents vehicle management system interactions and subsystem management functions. Each of the subsystems have the following functions: performance, diagnostics, prognostics, monitoring, and control.This paper presents a framework which conceptualizes how these subsystems interact with the various system management functions. This is accomplished by supplying new information in real time to the vehicle avionics real-time to allow responses to vehicle subsystem failures and performance degradation. This framework can potentially achieve autonomous operation capabilities necessary to assure crew safety and mission safety.
This article describes a novel scheme for autonomous component health management (ACHM) with failed actuator detection and identification and failed sensor detection, identification, and avoidance. This new scheme has features that are very superior to those with triple redundant sensing and voting, yet requires fewer sensors; it can be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided in this article. Simulation results for its application to a single-axis spacecraft attitude control system with a third-order plant and dual-redundant measurements of the system states are presented. The ACHM scheme fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is autonomous; is adaptive; works in real time; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures, reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open-and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary estimates of the system states, detects and identifies failed sensors and actuators, and avoids failed sensors in generating preliminary estimates of the system states with a fixed-gain Kalman filter that provides model-based state estimates, which are utilized in the failure detection logic, and generates optimal estimates for the system states. The simulation results show that ACHM can successfully detect, identify, and avoid sensor failures that are single or multiple; persistent and intermittent; and hard-over, soft, and zero-output types. It is now ready to be tested on a computer model of an actual system.
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