NASA Logo, External Link
Facebook icon, External Link to NASA STI page on Facebook Twitter icon, External Link to NASA STI on Twitter YouTube icon, External Link to NASA STI Channel on YouTube RSS icon, External Link to New NASA STI RSS Feed AddThis share icon
 

Record Details

Record 30 of 28892
Intelligent neuroprocessors for in-situ launch vehicle propulsion systems health management
Author and Affiliation:
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)
Abstract: Efficacy 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.
Publication Date: Jan 01, 1993
Document ID:
19930013032
(Acquired Dec 28, 1995)
Accession Number: 93N22221
Subject Category: CYBERNETICS
Coverage: Abstract Only
Document Type: Conference Paper
Publication Information: NASA. Johnson Space Center, Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, Volume 2; p 346-347
Publisher Information: United States
Financial Sponsor: NASA; United States
Organization Source: Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States
Description: 2p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: No Copyright
NASA Terms: FAULT TOLERANCE; MASSIVELY PARALLEL PROCESSORS; NEURAL NETS; PROPULSION SYSTEM CONFIGURATIONS; SIGNATURE ANALYSIS; SYSTEMS HEALTH MONITORING; SYSTEMS MANAGEMENT; HEAVY LIFT LAUNCH VEHICLES; PROPULSION SYSTEM PERFORMANCE; SPACE SHUTTLES
Imprint And Other Notes: In NASA. Johnson Space Center, Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, Volume 2 p 346-347 (SEE N93-22206 08-63)
Availability Source: Other Sources
› Back to Top
Find Similar Records
NASA Logo, External Link
NASA Official: Gerald Steeman
Site Curator: STI Program
Last Modified: August 27, 2013
Contact Us