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Redundancy Management Technique for Space Shuttle Computers

Sklaroff, J. R.·1976·IBM Journal of Research and Development

Abstract

This paper describes how a set of off-the-shelf general purpose digital computers is being managed in a redundant avionic configuration while performing flight-critical functions for the Space Shuttle. The description covers the architecture of the redundant computer set, associated redundancy design requirements, and the technique used to detect a failed computer and to identify this failure on-board to the crew. Significant redundancy management requirements consist of imposing a total failure coverage on all flight-critical functions, when more than two redundant computers are operating in flight, and a maximum failure coverage for limited storage and processing time, when only two are operating. The basic design technique consists of using dedicated redundancy management hardware and software to allow each computer to judge the “health” of the others by comparing computer outputs and to “vote” on the judgments. In formulating the design, hardware simplicity, operational flexibility, and minimum computer resource utilization were used as criteria.

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Metadata

Authors
Sklaroff, J. R.
Venue
IBM Journal of Research and Development
Publisher
IBM
Published
1976-01
Volume
20
Issue
1
Pages
20-28
ISSN
0018-8646 (ISSN-L)
OpenAlex
W2026428156
Cited by
162
References
0
FWCI
4.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Redundancy Management Technique for Space Shuttle Computers. https://app.cochat.ai/papers/10.1147/rd.201.0020
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Sklaroff, J. R. (1976). Redundancy Management Technique for Space Shuttle Computers. IBM Journal of Research and Development. https://doi.org/10.1147/rd.201.0020

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.