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Detection and Prognostics on Low Dimensional Systems

Metadata Updated: December 7, 2023

This paper describes the application of known and novel prognostic algorithms on systems that can be described by low dimensional, potentially nonlinear dynamics. The methods rely on estimating the conditional probability distribution of the output of the system at a future time given knowledge of the current state of the system. We show how to estimate these conditional probabilities using a variety of techniques, including bagged neural networks and kernel methods such as Gaussian Process Regression (GPR). The results are compared with standard method such as the nearest neighbor algorithm. We demonstrate the algorithms on a real-world data set and a simulated data set. The real-world data set consists of the intensity of an NH3 laser. The laser data set has been shown by other authors to exhibit low-dimensional chaos with sudden drops in intensity. The simulated data set is generated from the Lorenz attractor and has known statistical characteristics. On these data sets, we show the evolution of the estimated conditional probability distribution, the way it can act as a prognostic signal, and its use as an early warning system. We also review a novel approach to perform Gaussian Process Regression with large numbers of data points.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Identifier DASHLINK_148
Data First Published 2010-09-22
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id d460d8fe-81e4-4141-977b-7683579668e0
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash 3c0ec4c5c37c2483cf381b28bcc6b8f17e363b59ea45b1f636e9c6bf7d3e8941
Source Schema Version 1.1

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