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  • 1
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    Satellite material contaminant optical properties (1990)
    In:  CASI
    Details
    Publication Date: 2013-08-31
    Description: The Air Force Wright Research and Development Center and the Arnold Engineering Development Center are continuing a program for measuring optical effects of satellite material outgassing products on cryo-optic surfaces. Presented here are infrared (4000 to 700 cm(-1)) transmittance data for contaminant films condensed on a 77 K geranium window. From the transmittance data, the contaminant film refractive and absorptive indices (n, k) were derived using an analytical thin-film interference model with a nonlinear least-squares algorithm. To date 19 materials have been studied with the optical contents determined for 13 of those. The materials include adhesives, paints, composites, films, and lubricants. This program is continuing and properties for other materials will be available in the future.
    Keywords: OPTICS
    Type: NASA, Lyndon B. Johnson Space Center, Third Annual Workshop on Space Operations Automation and Robotics (SOAR 1989); p 257-262
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    Classification of data patterns using an autoassociative neural network topology (1989)
    In:  Other Sources
    Details
    Publication Date: 2019-07-13
    Description: 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.
    Keywords: CYBERNETICS
    Type: IEA/AIE-89; Jun 06, 1989 - Jun 09, 1989; Tullahoma, TN; United States
    Format: text
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    NASA TECHNICAL REPORTS
  • 3
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    Pattern-based fault diagnosis using neural networks (1988)
    In:  Other Sources
    Details
    Publication Date: 2019-07-13
    Description: 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.
    Keywords: CYBERNETICS
    Type: International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems; Jun 01, 1988 - Jun 03, 1988; Tullahoma, TN; United States
    Format: text
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    NASA TECHNICAL REPORTS
  • 4
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    Detecting and diagnosing SSME faults using an autoassociative neural network topology (1989)
    In:  CASI
    Details
    Publication Date: 2019-08-16
    Description: 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.
    Keywords: Cybernetics
    Type: Overview of the Center for Advanced Space Propulsion; NASA-CR-199690
    Format: text
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    NASA TECHNICAL REPORTS
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