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Mackey, Ryan

Publications and source records attributed to Mackey, Ryan.

28 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Flight-Tested Prototype of BEAM Software

Researchers at JPL have completed a software prototype of BEAM (Beacon-based Exception Analysis for Multi-missions) and successfully tested its operation in flight onboard a NASA research aircraft. BEAM (see NASA Tech Briefs, Vol. 26, No. 9; and Vol. 27, No. 3) is an ISHM (Integrated Systems Health Management) technology that automatically analyzes sensor data and classifies system behavior as either nominal or anomalous, and further characterizes anomalies according to strength, duration, and affected signals. BEAM (see figure) can be used to monitor a wide variety of physical systems and sensor types in real time. In this series of tests, BEAM monitored the engines of a Dryden Flight Research Center F-18 aircraft, and performed onboard, unattended analysis of 26 engine sensors from engine startup to shutdown. The BEAM algorithm can detect anomalies based solely on the sensor data, which includes but is not limited to sensor failure, performance degradation, incorrect operation such as unplanned engine shutdown or flameout in this example, and major system faults. BEAM was tested on an F-18 simulator, static engine tests, and 25 individual flights totaling approximately 60 hours of flight time. During these tests, BEAM successfully identified planned anomalies (in-flight shutdowns of one engine) as well as minor unplanned anomalies (e.g., transient oil- and fuel-pressure drops), with no false alarms or suspected false-negative results for the period tested. BEAM also detected previously unknown behavior in the F- 18 compressor section during several flights. This result, confirmed by direct analysis of the raw data, serves as a significant test of BEAM's capability.

Mackey, Ryan↗
NASA NTRS2006

Integrated System Health Management (ISHM) Technology Demonstration Project Final Report

Integrated System Health Management (ISHM) is an essential capability that will be required to enable upcoming explorations mission systems such as the Crew Exploration Vehicle (CEV) and Crew Launch Vehicle (CLV), as well as NASA aeronautics missions. However, the lack of flight experience and available test platforms have held back the infusion by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL) of ISHM technologies into future space and aeronautical missions. To address this problem, a pioneer project was conceived to use a high-performance aircraft as a low-cost proxy to develop, mature, and verify the effectiveness of candidate ISHM technologies. Given the similarities between spacecraft and aircraft, an F/A-18 currently stationed at Dryden Flight Research Center (DFRC) was chosen as a suitable host platform for the test bed. This report describes how the test bed was conceived, how the technologies were integrated on to the aircraft, and how these technologies were matured during the project. It also describes the lessons learned during the project and a forward path for continued work.

Mackey, Ryan↗
NASA NTRS2005

Intelligent Elements for the ISHM Testbed and Prototypes (ITP) Project

Deep-space manned missions will require advanced automated health assessment capabilities. Requirements such as in-space assembly, long dormant periods and limited accessibility during flight, present significant challenges that should be addressed through Integrated System Health Management (ISHM). The ISHM approach will provide safety and reliability coverage for a complete system over its entire life cycle by determining and integrating health status and performance information from the subsystem and component levels. This paper will focus on the potential advanced diagnostic elements that will provide intelligent assessment of the subsystem health and the planned implementation of these elements in the ISHM Testbed and Prototypes (ITP) Project under the NASA Exploration Systems Research and Technology program.

Maul, William A.↗
NASA NTRS2005

Advances in integrated system heath management system technologies : overview of NASA and industry collaborative activities

This paper will describe recent advances in ISHM technologies made through collaboration between NASA and industry. In particular, the paper will focus on past, present, and future technology development and maturation efforts at the Jet Propulsion Laboratory (JPL) and its industry partner, Northrop Grumman lntegrated Systems (NGIS).

integrated system health management↗
NASA NTRS2005

Fast Algorithms for Model-Based Diagnosis

Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.

Fijany, Amir↗
NASA NTRS2003

Active State Model for Autonomous Systems

The concept of the active state model (ASM) is an architecture for the development of advanced integrated fault-detection-and-isolation (FDI) systems for robotic land vehicles, pilotless aircraft, exploratory spacecraft, or other complex engineering systems that will be capable of autonomous operation. An FDI system based on the ASM concept would not only provide traditional diagnostic capabilities, but also integrate the FDI system under a unified framework and provide mechanism for sharing of information between FDI subsystems to fully assess the overall health of the system. The ASM concept begins with definitions borrowed from psychology, wherein a system is regarded as active when it possesses self-image, self-awareness, and an ability to make decisions itself, such that it is able to perform purposeful motions and other transitions with some degree of autonomy from the environment. For an engineering system, self-image would manifest itself as the ability to determine nominal values of sensor data by use of a mathematical model of itself, and selfawareness would manifest itself as the ability to relate sensor data to their nominal values. The ASM for such a system may start with the closed-loop control dynamics that describe the evolution of state variables. As soon as this model was supplemented with nominal values of sensor data, it would possess self-image. The ability to process the current sensor data and compare them with the nominal values would represent self-awareness. On the basis of self-image and self-awareness, the ASM provides the capability for self-identification, detection of abnormalities, and self-diagnosis.

Park, Han↗
NASA NTRS2003

Prognostics Methodology for Complex Systems

An automatic method to schedule maintenance and repair of complex systems is produced based on a computational structure called the Informed Maintenance Grid (IMG). This method provides solutions to the two fundamental problems in autonomic logistics: (1) unambiguous detection of deterioration or impending loss of function and (2) determination of the time remaining to perform maintenance or other corrective action based upon information from the system. The IMG provides a health determination over the medium-to-longterm operation of the system, from one or more days to years of study. The IMG is especially applicable to spacecraft and both piloted and autonomous aircraft, or industrial control processes.

Gulati, Sandeep↗
NASA NTRS2003

Integrated Formulation of Beacon-Based Exception Analysis for Multimissions

Further work on beacon-based exception analysis for multimissions (BEAM), a method of real-time, automated diagnosis of a complex electromechanical systems, has greatly expanded its capability and suitability of application. This expanded formulation, which fully integrates physical models and symbolic analysis, is described. The new formulation of BEAM expands upon previous advanced techniques for analysis of signal data, utilizing mathematical modeling of the system physics, and expert-system reasoning,

Mackey, Ryan↗
NASA NTRS2003

Wavelet-Based Real-Time Diagnosis of Complex Systems

A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

Gulati, Sandeep↗