This study proposes a noise-robust unsupervised acoustic anomaly detection framework for early identification of abnormal operating conditions in rotating machinery under submarine-like environments with severe data scarcity. In such environments, un...
In semiconductor and optoelectronics manufacturing, the reliability of turbo molecular pumps (TMPs) is vital for maintaining vacuum integrity and ensuring product yield. However, acoustic monitoring in cleanrooms faces severe challenges due to ambien...
Distributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from...
Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anoma...
Monitoring the condition of belt conveyor idlers is critical for ensuring safe and efficient operation of industrial conveying systems. However, existing methods often suffer from limited scalability and delayed fault detection, particularly under va...
Deep learning anomaly detection is important in distributed optical fiber acoustic sensing (DAS). However, anomaly detection is more challenging than traditional learning tasks, due to the scarcity of true-positive data and the vast imbalance and irr...
An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In rece...
Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal...
Unsupervised anomalous sound detection aims to learn acoustic features solely from the operational sounds of normal equipment and identify potential anomalies based on these features. Recent self-supervised classification frameworks based on machine...
Industrial pumps are critical components in manufacturing and process plants, where early acoustic anomaly detection is essential for preventing unplanned downtime and reducing maintenance costs. In practice, however, strong background noise, severe...
Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the qua...
Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasin...
Underground petrochemical pipelines face severe risks from unreported third-party damage during excavation. Existing monitoring systems based on supervised deep learning or distributed acoustic sensing often require expensive hardware and large volum...
End-of-Line (EoL) quality inspection of electric motors requires reliable detection of manufacturing faults before products leave the production line. However, conventional supervised deep learning approaches depend on a sufficient number of labeled...
Artificial intelligence (AI) is increasingly adopted in manufacturing for tasks such as automated inspection, predictive maintenance, and condition monitoring. However, the opaque, black-box nature of many AI models remains a major barrier to industr...