Non-elastic time series fuzzy clustering for efficient analysis of industrial data sets

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

In line with the green transition and digitalization trends, there is an increasing need for effective performance and health monitoring of production machines. Machine operation footprints resonate in quantities such as supply current and pneumatic line pressure, providing valuable insights when analyzed. Examining these low-level signals allows for monitoring and identification of changes in machine operation over time. A critical step in analyzing machine data is clustering, requiring an effective method for calculating time series cluster centroids. This paper introduces Error in Aligned series (ERAL), a novel method that generates a time series centroid that distills the fundamental shape of the underlying datasets. ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods. Our analysis shows that existing methods can create artificial spikes and plateaus, which ERAL mitigates; it remains faithful to the original data shape. Additionally, ERAL offers improvements in computational efficiency and prototype quality. We evaluate the method against Dynamic Time Warping (DTW)-based methods across various datasets, and apply it to a time series clustering task using a real-world industrial dataset. In combination with a fuzzy clustering algorithm, ERAL generates visually convincing clusters. By leveraging fuzzy membership concepts, it achieves robust and adaptable clustering outcomes that reflect real-world data complexity and ambiguity.

• Language: English
• Keywords: time series analysis, time series alignment and averaging, prototype, fuzzy clustering, dynamic time warping
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 18 str.
• Numbering: Vol. 167, pt. B, art. 112398
• PID: 20.500.12556/RUL-164684
• UDC: 53
• ISSN on article: 1568-4946
• DOI: 10.1016/j.asoc.2024.112398
• COBISS.SI-ID: 213912579
• Publication date in RUL: 07.11.2024

Record is a part of a journal

• Title: Applied soft computing
• Publisher: Elsevier
• ISSN: 1568-4946
• COBISS.SI-ID: 16080679

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: analiza časovnih vrst, časovna os, mehko rojenje