Industry 4.0
Vol. 10 (2025), Issue 3, pg(s) 115-118

DOMINANT TECHNOLOGIES IN “INDUSTRY 4.0”

Correlation-Based Sensor Pruning and Malfunction Detection in Multi-Sensor Condition Monitoring

  • 1 AGH University of Krakow, Poland

Abstract

This study presents a correlation-based approach for both detecting sensor malfunctions and identifying redundant sensors in a multi-sensor condition monitoring system. Sensor malfunctions were detected using a threshold-based method that flagged correlation drops, with persistence criteria applied to eliminate false positives. While no persistent malfunctions were observed during the study, the developed algorithm remains suitable for real-time deployment. Correlation analysis also revealed that the 3axis_Y signal exhibited the highest average correlation with others, indicating redundancy. Five machine learning models were trained and evaluated with the Leave-
One-Run-Out strategy to guarantee generalization across acquisition sessions. The findings demonstrated that correlation-driven sensor selection and anomaly detection are effective tools for optimizing predictive maintenance systems, improving model generalization, and simplifying sensor networks without sacrificing reliability.

Keywords

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