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Bickmore, Timothy W.

Publications and source records attributed to Bickmore, Timothy W..

SSME HPOTP post-test diagnostic system enhancement project

An assessment of engine and component health is routinely made after each test or flight firing of a space shuttle main engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project is to further develop a computer program which automates the analysis of test data from the SSME high-pressure oxidizer turbopump (HPOTP) in order to detect and diagnose anomalies. This program fits into a larger system, the SSME Post-Test Diagnostic System (PTDS), which will eventually be extended to assess the health and status of most SSME components on the basis of test data analysis. The HPOTP module is an expert system, which uses 'rules-of-thumb' obtained from interviews with experts from NASA Marshall Space Flight Center (MSFC) to detect and diagnose anomalies. Analyses of the raw test data are first performed using pattern recognition techniques which result in features such as spikes, shifts, peaks, and drifts being detected and written to a database. The HPOTP module then looks for combination of these features which are indicative of known anomalies, using the rules gathered from the turbomachinery experts. Results of this analysis are then displayed via a graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each.

Bickmore, Timothy W.

Real-time sensor data validation

This report describes the status of an on-going effort to develop software capable of detecting sensor failures on rocket engines in real time. This software could be used in a rocket engine controller to prevent the erroneous shutdown of an engine due to sensor failures which would otherwise be interpreted as engine failures by the control software. The approach taken combines analytical redundancy with Bayesian belief networks to provide a solution which has well defined real-time characteristics and well-defined error rates. Analytical redundancy is a technique in which a sensor's value is predicted by using values from other sensors and known or empirically derived mathematical relations. A set of sensors and a set of relations among them form a network of cross-checks which can be used to periodically validate all of the sensors in the network. Bayesian belief networks provide a method of determining if each of the sensors in the network is valid, given the results of the cross-checks. This approach has been successfully demonstrated on the Technology Test Bed Engine at the NASA Marshall Space Flight Center. Current efforts are focused on extending the system to provide a validation capability for 100 sensors on the Space Shuttle Main Engine.

Bickmore, Timothy W.

A qualitative approach to systemic diagnosis of the SSME

A generic software architecture has been developed for posttest diagnostics of rocket engines, and is presently being applied to the posttest analysis of the SSME. This investigation deals with the Systems Section module of the architecture, which is presently under development. Overviews of the manual SSME systems analysis process and the overall SSME diagnostic system architecture are presented.

Bickmore, Timothy W.

An evidential approach to model-based satellite diagnosis

The paper presents an approach to the implementation of an automated satellite diagnosis capability which emphasizes techniques offering flexibility in the application of diagnostic strategies. The system utilizes a structural and behavioral model of the satellite; it uses a form of spreading activation to perform the diagnostic procedures on the model. A Dempster-Shafer evidential framework was implemented, making it possible to integrate evidence from the multiple sources necessary for building useful systems.

Bickmore, Timothy W.