Engineering topics

Dietz, W. E.

Publications and source records attributed to Dietz, W. E..

7 records

NASA NTRS1995

SSME component assembly and life management expert system

The Space Shuttle utilizes several rocket engine systems, all of which must function with a high degree of reliability for successful mission completion. The Space Shuttle Main Engine (SSME) is by far the most complex of the rocket engine systems. In earlier spacecraft, rocket systems (and, in fact, the entire spacecraft) were designed for use on only a single mission and were discarded after use. In a major departure from earlier practices, almost all systems on the Space Shuttle, including the rocket systems, are designed to be reusable; only the external tank is discarded during each mission.

Ali, M.↗

NASA NTRS1989

SSME component assembly and life management expert system

The space shuttle utilizes several rocket engine systems, all of which must function with a high degree of reliability for successful mission completion. The space shuttle main engine (SSME) is by far the most complex of the rocket engine systems and is designed to be reusable. The reusability of spacecraft systems introduces many problems related to testing, reliability, and logistics. Components must be assembled from parts inventories in a manner which will most effectively utilize the available parts. Assembly must be scheduled to efficiently utilize available assembly benches while still maintaining flight schedules. Assembled components must be assigned to as many contiguous flights as possible, to minimize component changes. Each component must undergo a rigorous testing program prior to flight. In addition, testing and assembly of flight engines and components must be done in conjunction with the assembly and testing of developmental engines and components. The development, testing, manufacture, and flight assignments of the engine fleet involves the satisfaction of many logistical and operational requirements, subject to many constraints. The purpose of the SSME Component Assembly and Life Management Expert System (CALMES) is to assist the engine assembly and scheduling process, and to insure that these activities utilize available resources as efficiently as possible.

Ali, M.↗

NASA NTRS1989

Detecting and diagnosing SSME faults using an autoassociative neural network topology

An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.

Ali, M.↗

NASA NTRS1989

Space Shuttle Main Engine component assembly, assignment, and scheduling expert system

The SSME's Component Assembly and Life Management Expert System (CALMES) assists the engine assembly and scheduling process, ensuring that these activities utilize available resources with the greatest possible efficiency. On the basis of parts inventories and a proposed flight schedule, CALMES (1) determined how components may be optimally assembled from the parts inventory, (2) assigns components to flights, (3) schedules component testing, and (4) schedules component assembly. A graph-theoretical optimal matching algorithm, based on a modified simplex method, is applied to the major functions required by the SSME component assembly and scheduling processes.

Dietz, W. E.↗

NASA NTRS1989

Classification of data patterns using an autoassociative neural network topology

A diagnostic expert system based on neural networks is developed and applied to the real-time diagnosis of jet and rocket engines. The expert system methodologies are based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. The approach addresses deficiencies inherent in many feedforward neural network models and greatly reduces the number of networks necessary to identify the existence of a fault condition and estimate the duration and severity of the identified fault. The network topology used in the present implementation of the diagnostic system is described, as well as the training regimen used and the response of the system to inputs representing both previously observed and unknown fault scenarios. Noise effects on the integrity of the diagnosis are also evaluated.

Dietz, W. E.↗

NASA NTRS1988

Pattern-based fault diagnosis using neural networks

An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.

Dietz, W. E.↗

NASA NTRS1987

Qualitative and temporal reasoning in engine behavior analysis

Numerical simulation models, engine experts, and experimental data are used to generate qualitative and temporal representations of abnormal engine behavior. Engine parameters monitored during operation are used to generate qualitative and temporal representations of actual engine behavior. Similarities between the representations of failure scenarios and the actual engine behavior are used to diagnose fault conditions which have already occurred, or are about to occur; to increase the surveillance by the monitoring system of relevant engine parameters; and to predict likely future engine behavior.

Dietz, W. E.↗