Early Failure Detection for Predictive Maintenance of Sensor Parts

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Early Failure Detection for Predictive Maintenance of Sensor Parts

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Early Failure Detection for Predictive Maintenance of Sensor Parts

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

CEUR workshop proceedings

ISSN

1613-0073

e-ISSN

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Svazek periodika

2016

Číslo periodika v rámci svazku

1649

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

123-130

Kód UT WoS článku

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EID výsledku v databázi Scopus

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