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Pattern-recognition techniques applied to performance monitoring of the DSS 13 34-meter antenna control assemblyThe results of applying pattern recognition techniques to diagnose fault conditions in the pointing system of one of the Deep Space network's large antennas, the DSS 13 34-meter structure, are discussed. A previous article described an experiment whereby a neural network technique was used to identify fault classes by using data obtained from a simulation model of the Deep Space Network (DSN) 70-meter antenna system. Described here is the extension of these classification techniques to the analysis of real data from the field. The general architecture and philosophy of an autonomous monitoring paradigm is described and classification results are discussed and analyzed in this context. Key features of this approach include a probabilistic time-varying context model, the effective integration of signal processing and system identification techniques with pattern recognition algorithms, and the ability to calibrate the system given limited amounts of training data. Reported here are recognition accuracies in the 97 to 98 percent range for the particular fault classes included in the experiments.
Document ID
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Mellstrom, J. A.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Smyth, P.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
September 6, 2013
Publication Date
August 15, 1991
Publication Information
Publication: The Telecommunications and Data Acquisition Report
Subject Category
Communications And Radar
Accession Number
Funding Number(s)
PROJECT: RTOP 310-20-65-91-00
Distribution Limits
Work of the US Gov. Public Use Permitted.
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