Outlier Detection in Sensor Data using Ensemble Learning

Research output: Contribution to journalConference articlepeer-review

Abstract

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance.

Original languageEnglish
JournalProcedia Computer Science
Volume176
Pages (from-to)1160-1169
Number of pages10
ISSN1877-0509
DOIs
Publication statusPublished - 2020
Event24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - Virtuel konference
Duration: 16 Sept 202018 Sept 2020

Conference

Conference24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
CityVirtuel konference
Period16/09/2018/09/20
OtherKES2020 conference will be presented as a virtual conference.

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