Metrics for Evaluating Performance of Prognostics Techniques

Metadata Updated: July 17, 2020

Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.

Access & Use Information

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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Dates

Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
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 July 17, 2020
Publisher Dashlink
Unique Identifier DASHLINK_393
Maintainer
EDWARD BALABAN
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 9bf98280-daca-4841-9f7e-d266c177d36a
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2011-06-07
Homepage URL https://c3.nasa.gov/dashlink/resources/393/
Data Last Modified 2020-01-29
Program Code 026:029
Source Datajson Identifier True
Source Hash 6e7410c81fdb8c0d6d8b9e0e71071ea54b4f41d0
Source Schema Version 1.1

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