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Erschienen in:
Fault Tolerant Data Stream Processing in Cooperation with OLTP Engine Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Applying Big Data Intelligence for Real Time Machine Fault Prediction

verfasst von : Amrit Pal, Manish Kumar

Erschienen in: Big Data Analytics

Verlag: Springer International Publishing

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Abstract

Continuous use of mechanical systems requires precise maintenance. Automatic monitoring of such systems generates a large amount of data which require intelligent mining methods for processing and information extraction. The problem is to predict the faults generated with ball bearing which severely degrade operating conditions of machinery. We develop a distributed fault prediction model based on big data intelligence that extracts nine essential features from ball bearing dataset through distributed random forest. We also perform a rigorous simulation analysis of the proposed approach and the results ensure the accuracy/correctness of the method. Different types of fault classes are considered for prediction purpose and classification is done in a supervised distributed environment.

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Metadaten
Titel
Applying Big Data Intelligence for Real Time Machine Fault Prediction
verfasst von
Amrit Pal
Manish Kumar
Copyright-Jahr
2018
Buch
Big Data Analytics

Print ISBN: 978-3-030-04779-5

Electronic ISBN: 978-3-030-04780-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-04780-1_26