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Intelligent neuroprocessors for in-situ launch vehicle propulsion systems health managementEfficacy of existing on-board propulsion systems health management systems (HMS) are severely impacted by computational limitations (e.g., low sampling rates); paradigmatic limitations (e.g., low-fidelity logic/parameter redlining only, false alarms due to noisy/corrupted sensor signatures, preprogrammed diagnostics only); and telemetry bandwidth limitations on space/ground interactions. Ultra-compact/light, adaptive neural networks with massively parallel, asynchronous, fast reconfigurable and fault-tolerant information processing properties have already demonstrated significant potential for inflight diagnostic analyses and resource allocation with reduced ground dependence. In particular, they can automatically exploit correlation effects across multiple sensor streams (plume analyzer, flow meters, vibration detectors, etc.) so as to detect anomaly signatures that cannot be determined from the exploitation of single sensor. Furthermore, neural networks have already demonstrated the potential for impacting real-time fault recovery in vehicle subsystems by adaptively regulating combustion mixture/power subsystems and optimizing resource utilization under degraded conditions. A class of high-performance neuroprocessors, developed at JPL, that have demonstrated potential for next-generation HMS for a family of space transportation vehicles envisioned for the next few decades, including HLLV, NLS, and space shuttle is presented. Of fundamental interest are intelligent neuroprocessors for real-time plume analysis, optimizing combustion mixture-ratio, and feedback to hydraulic, pneumatic control systems. This class includes concurrently asynchronous reprogrammable, nonvolatile, analog neural processors with high speed, high bandwidth electronic/optical I/O interfaced, with special emphasis on NASA's unique requirements in terms of performance, reliability, ultra-high density ultra-compactness, ultra-light weight devices, radiation hardened devices, power stringency, and long life terms.
Document ID
19930013032
Document Type
Conference Paper
Authors
Gulati, S. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Tawel, R. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Thakoor, A. P. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1993
Publication Information
Publication: NASA. Johnson Space Center, Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, Volume 2
Subject Category
CYBERNETICS
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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IDRelationTitle19930013017Analytic PrimaryProceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2