Citation
Abstract
This article describes the 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-m structure. 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 DSN 70-m antenna system. This article describes 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. The article reports recognition accuracies in the 97-98-percent range for the particular fault classes included in the experiments.
Details
- Volume
- 42-106
- Published
- August 15, 1991
- Pages
- 30–51
- File Size
- 1.1 MB