Early Failure Detection for Predictive Maintenance of Sensor Parts

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00306815" target="_blank" >RIV/68407700:21240/16:00306815 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-1649/123.pdf" target="_blank" >http://ceur-ws.org/Vol-1649/123.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Early Failure Detection for Predictive Maintenance of Sensor Parts

Original language description

Maintenance of a sensor part typically means renewal of the sensor in regular intervals or replacing the malfunctioning sensor. However optimal timing of the replacement can reduce maintenance costs. The aim of this article is to suggest a predictive maintenance strategy for sensors using condition monitoring and early failure detection based on their own collected measurements. Three different approaches that deal with early failure detection of sensor parts are introduced 1) approach based on feature extraction and status classification, 2) approach based on time series modeling and 3) approach based on anomaly detection using autoencoders. All methods were illustrated on real-world data and were proven to be applicable for condition monitoring.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

CEUR workshop proceedings

ISSN

1613-0073

e-ISSN

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Volume of the periodical

2016

Issue of the periodical within the volume

1649

Country of publishing house

DE - GERMANY

Number of pages

8

Pages from-to

123-130

UT code for WoS article

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EID of the result in the Scopus database

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