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

Stržinar, Žiga (author), Škrjanc, Igor (author), Pratama, Mahardhika (author), Pregelj, Boštjan (author)

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.

• Language: English
• Keywords: time series analysis, evolving clustering, time series clustering, data stream, industrial data
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 16 str.
• Numbering: Vol. 209, art. 111508
• PID: 20.500.12556/RUL-182664
• UDC: 004.9
• ISSN on article: 1879-0550
• DOI: 10.1016/j.cie.2025.111508
• COBISS.SI-ID: 248437763
• Publication date in RUL: 20.05.2026

Record is a part of a journal

• Title: Computers & industrial engineering
• Publisher: Elsevier
• ISSN: 1879-0550
• COBISS.SI-ID: 118838275

Licences

• License: CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
• Link: http://creativecommons.org/licenses/by-nc/4.0/
• Description: A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.

Secondary language

• Language: Slovenian
• Keywords: analiza časovnih vrst, industrija, podatki, proizvodnja, stroji