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...
The objective of this research was to evaluate a machine learning– based methodology for the early detection of health abnormalities in cattle by analyzing physiological and feeding behavior variables obtained from IoT sensors in real–world settings. For this research, a synthetic dataset of body temperature, heart rat...
Anomaly-detection methods typically fail to detect changes in operating conditions or are unresponsive in detecting changes in the aging of industrial assets. This paper outlines an unsupervised lightweight machine learning-based approach to adaptively detect anomalies in the reliability of industrial assets. It incorp...
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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