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Evolving clustering of time series for unsupervised analysis of industrial data streams.

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Title: Evolving clustering of time series for unsupervised analysis of industrial data streams.
Authors: Stržinar, Žiga1,2 (AUTHOR) ziga.strzinar@ijs.si, Škrjanc, Igor2 (AUTHOR), Pratama, Mahardika3 (AUTHOR), Pregelj, Boštjan1 (AUTHOR)
Source: Computers & Industrial Engineering. Nov2025, Vol. 209, pN.PAG-N.PAG. 1p.
Subject Terms: *TIME series analysis, *REAL-time computing, *QUANTITATIVE research, CLUSTERING algorithms, FAULT diagnosis
Abstract: In industrial real-time process monitoring and fault detection, detecting and clustering machine events from time series data are essential tasks. However, conventional clustering methods often require the number of clusters to be known in advance, which is impractical in dynamic, real-world industrial scenarios. Therefore, online evolving methods are required. Furthermore, many existing methods used to obtain cluster prototypes generate prototypes containing unwanted artifacts. This paper introduces Streaming Error in Aligned Series (sERAL), a novel method for online time series alignment and averaging. sERAL aims to produce cluster prototypes that accurately represent the underlying data shape, a property often overlooked by competing methods. sERAL is integrated into an evolving time series clustering algorithm capable of unsupervised real-time clustering of time series streams. The method enables dynamic adaptation of both individual clusters and the number of clusters through updating and merging mechanisms. The proposed sERAL method is evaluated using sensor data collected from a real-world industrial manufacturing process. Comparison with established alignment and averaging methods reveals that sERAL produces improved cluster prototypes with fewer erroneous shape artifacts, which are common in Dynamic Time Warping-based methods. Complexity analysis shows that sERAL is highly scalable. This work addresses a significant gap in time series clustering for streaming applications, offering a practical and scalable solution for industrial use cases where signal shape is crucial. The sERAL algorithm and the associated clustering method are made available as an open-source Python package to encourage broad use. • A novel online time series alignment and averaging method — Streaming Error in Aligned Series (sERAL) is introduced. • sERAL is applied to evolving clustering algorithm for time series data. • Python source code for sERAL is made publicly available https://repo.ijs.si/zstrzinar/streaming-eral. • We use a publicly available dataset enabling full replication of our results. [ABSTRACT FROM AUTHOR]
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Database: Business Source Index
Description
Abstract:In industrial real-time process monitoring and fault detection, detecting and clustering machine events from time series data are essential tasks. However, conventional clustering methods often require the number of clusters to be known in advance, which is impractical in dynamic, real-world industrial scenarios. Therefore, online evolving methods are required. Furthermore, many existing methods used to obtain cluster prototypes generate prototypes containing unwanted artifacts. This paper introduces Streaming Error in Aligned Series (sERAL), a novel method for online time series alignment and averaging. sERAL aims to produce cluster prototypes that accurately represent the underlying data shape, a property often overlooked by competing methods. sERAL is integrated into an evolving time series clustering algorithm capable of unsupervised real-time clustering of time series streams. The method enables dynamic adaptation of both individual clusters and the number of clusters through updating and merging mechanisms. The proposed sERAL method is evaluated using sensor data collected from a real-world industrial manufacturing process. Comparison with established alignment and averaging methods reveals that sERAL produces improved cluster prototypes with fewer erroneous shape artifacts, which are common in Dynamic Time Warping-based methods. Complexity analysis shows that sERAL is highly scalable. This work addresses a significant gap in time series clustering for streaming applications, offering a practical and scalable solution for industrial use cases where signal shape is crucial. The sERAL algorithm and the associated clustering method are made available as an open-source Python package to encourage broad use. • A novel online time series alignment and averaging method — Streaming Error in Aligned Series (sERAL) is introduced. • sERAL is applied to evolving clustering algorithm for time series data. • Python source code for sERAL is made publicly available https://repo.ijs.si/zstrzinar/streaming-eral. • We use a publicly available dataset enabling full replication of our results. [ABSTRACT FROM AUTHOR]
ISSN:03608352
DOI:10.1016/j.cie.2025.111508