Anomaly Detection using LSTM N. Networks and Naive Bayes Classifiers in Multi-Variate Time-Series Data from a Bolt Tightening Tool

Anomaly Detection using LSTM N. Networks and Naive Bayes Classifiers in Multi-Variate Time-Series Data from a Bolt Tightening Tool

In this thesis, an anomaly detection framework has been developed to aid in maintenance of tightening tools. The framework is built using LSTM networks and gaussian naive bayes  classifiers. The suitability of LSTM networks for multi-variate sensor data and time-series prediction as a basis for anom...

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Bibliographic Details
Main Author: Selander, Karl-Filip
Format: Others
Language:English
Published: KTH, Skolan för industriell teknik och management (ITM) 2021
Subjects:
LSTM
anomaly detection
time-series
multi-variable
sensor
deep learning
anomalidetektion
tidsserie
multivariabel
djupinlärning
Engineering and Technology
Teknik och teknologier
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303869

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303869

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