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Finding unusual energy consumption profiles from large scale data
(2020)
Our energy consumption is increasing every year. The new sensor-based smart measurement devices will provide an opportunity to measure this consumption accurately. This study focuses on finding anomalies from time-series ...
Detection and data-driven root cause analysis of paper machine drive anomalies
(2019)
The Industrial Internet has increased interest in the collection and utilization of data. The latter has become easier due to increased computing power and the development of analytical methods. The goal of this thesis is ...
Analysis of production testing data and detecting abnormal behavior
(2020)
This thesis presents methods to improve production testing methods by applying unsupervised machine learning to find anomalies from the data collected during testing. These methods are applied to a real-world case with the ...
Predictive maintenance Industry 4.0 : case Nokia
(2021)
Predictive maintenance is leading digital transformation to a highly self-optimized and automated environment for humans and machines to work together. In this research predictive maintenance and anomaly detection issues ...
Statistical fault characterization in industrial processes
(2021)
Fault detection is used to identify anomalous behavior in a process or machine. This study aims to characterize faults in an industrial process based on vibration signal. The studied data is unlabeled process data containing ...
Älykäs datan käyttö teollisuuden kunnossapidon ja käyttövarmuuden parantamisessa
(2019)
Paperitehtailla ja teollisuudessa yleisesti kerätään valtava määrä prosessien tuotanto- ja kunnossapitodataa. Tämän datan analysointi on perinteisin menetelmin sekä muuttujien- että datapisteiden määrän vuoksi erittäin ...
Detecting data quality issues in categorical data through anomaly detection
(2022)
Organizations have increasingly started to understand that data are one of their most important business assets. Nevertheless, for the data to be valuable, it has to be of good quality. Anomaly detection is one approach ...
Detecting temporal anomalies in time series data utilizing the matrix profile
(2022)
This work presents a review of anomaly detection algorithms and libraries and the strengths and weaknesses of the most commonly used benchmarking datasets. With this information, the experimental datasets are selected and ...
Anomaly detection in business metric monitoring
(2021)
In the digitalizing world, the amount of data transferred exceeds the human ability to study it manually. This is also the case for business metrics since data volume and the number of metrics to monitor is rapidly increasing. ...