Early Failure Detection for Predictive Maintenance of Sensor Parts
Result 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.
Keywords
predictive maintenanceearly failure detectioncondition monitoringmachine learningsensors
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
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Alternative languages
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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Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
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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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
IN - Informatics
Year of implementation
2016