An Event-based Distributed Diagnosis Framework using Structural Model Decomposition

Metadata Updated: May 2, 2019

Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.

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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 May 2, 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 May 2, 2019
Publisher Dashlink
Unique Identifier DASHLINK_921
Matthew Daigle
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
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Metadata Catalog ID
Schema Version
Catalog Describedby
Datagov Dedupe Retained 20190501230127
Harvest Object Id 62ef6411-be32-4327-a608-bb516dde08f8
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2014-08-11
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash ecb84b0cbdeac43b293f0487ed7f1e7d6af735fd
Source Schema Version 1.1

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