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Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators

Metadata Updated: December 7, 2023

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

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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 December 1, 2022
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 December 1, 2022
Metadata Updated Date December 7, 2023
Publisher Dashlink
Identifier DASHLINK_397
Data First Published 2011-06-08
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 44f8b7dd-2cf9-4578-ac37-37e2443018f0
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 ce442fd2eb3d15adee32ee8412c2dd27468f1a6dc7f6227f3101dcc604cf8749
Source Schema Version 1.1

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