Anomaly Detection for Complex Systems

Metadata Updated: February 28, 2019

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies.

However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions.

Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems.

For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

Access & Use Information

Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Dashlink
Unique Identifier DASHLINK_92
Nisheeth Srivastava
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id c7609560-b965-4e10-ba04-7f1937035381
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2010-09-10
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash 727a67a2e36e5e818ed49b1d79970e0916771278
Source Schema Version 1.1

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