Robust factor information fusion for industrial safety through TSFD-Based phase space reconstruction and noise reduction

•The research elucidates the fusion mechanism, proposes an SFEP analysis framework.•Explore the characteristics of SFEP and TSFBD, the research is conducted based on TSFBD.•Employ optimal phase space and KOLPP to address dimensionality and noise reduction. Industrial system safety is a core goal of...

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Veröffentlicht in:Information fusion Jg. 128; S. 103917
Hauptverfasser: Li, Shasha, Cui, Tiejun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2026
Schlagworte:
Data noise reduction
Factor classification
Industrial system safety
Robust factor information fusion
System fault evolution
Time series fault data (TSFD)
Factor classification
System fault evolution
Data noise reduction
Time series fault data (TSFD)
Industrial system safety
Robust factor information fusion
ISSN:1566-2535
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Zusammenfassung:•The research elucidates the fusion mechanism, proposes an SFEP analysis framework.•Explore the characteristics of SFEP and TSFBD, the research is conducted based on TSFBD.•Employ optimal phase space and KOLPP to address dimensionality and noise reduction. Industrial system safety is a core goal of industrial production, and robust factor information fusion is vital for data-driven industrial system fault prediction, early warning, and analysis. To explore system fault evolution characteristics, this paper constructs time series fault data (TSFD) by integrating multi- source industrial fault-impacting factor measurements, and proposes methods for optimal phase space construction, dimension reduction, noise reduction, and factor classification. It first analyzes system fault evolution process and TSFD characteristics, then presents the algorithm flow and validates via case study. For TSFD processing, optimal time delay and embedding dimension are determined to build the optimal phase space matrix; the KOLPP method retains local and global features to obtain key matrices, realizing TSFD optimization. Based on the robust factor information fusion method, the factor classification was obtained, including core factors, supplementary factors and irrelevant factors. Finally, algorithm challenges are discussed, and its characteristics summarized. This study enhances industrial fault management to ensure production reliability.
ISSN:1566-2535
DOI:10.1016/j.inffus.2025.103917