Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper presents an unsupervised multi-sensor framework that requires no fault labels...
Hydrogen refueling station (HRS) requires continuous safety monitoring, yet conventional management relies largely on periodic inspection and manual oversight, limiting proactive risk mitigation. This study presents a data-driven intelligent analysis platform for real-time monitoring and anomaly detection of HRS safety...
Reliable sensor data are fundamental to the effectiveness of long-term structural health monitoring (SHM) systems, in which measurement anomalies can compromise condition assessment, damage detection, and maintenance decision-making. This study presents a data separability-driven framework for automated anomaly classif...
This approach provides a methodological basis for the development of intelligent monitoring systems in precision livestock farming, with potential application in real–world scenarios through the incorporation of IoT sensor data, thus contributing to improved animal welfare and optimized bovine productivity.
The findings show that a self-recalibrating unsupervised model can successfully detect faults in a scalable manner, with an interpretable model, low computational complexity and can adapt to ageing assets and varying operating baselines without labelled fault information.
Air quality sensor networks need anomaly detection that works from day one—without training data, without historical baselines, and with results an operator can actually interpret. Existing methods (Isolation Forest, One-Class SVM, LOF) require representative “normal” data for training, which makes them brittle when en...
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