The detection and localization of damage in metallic structures using acoustic emission (AE)
monitoring and artificial intelligence technology such as deep learning has been widely …

The monitoring and evaluation of Alkali-silica reaction (ASR) damage in concrete structures
are required to ensure the serviceability and integrity of concrete infrastructures such as …

Additive manufacturing transforms the industry by integrating innovative and intelligent
technology, resulting in less material waste and faster prototyping. However, qualitative …

Alkali-silica reaction (ASR) is one of main damages causes in concrete structures such as
nuclear power plants which may endanger structural serviceability and integrity. Acoustic …

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to
redundant noise interference, and the difficulty in extracting fault feature signals from rolling …

Compared to vibration monitoring, acoustic emission (AE) monitoring in gas turbines is
highly sensitive to changes that do not involve whole-body motion, such as wear, rubbing …

Early damage detection and classification by condition monitoring systems is crucial to
enable predictive maintenance of manufacturing systems and industrial facilities. Data …

The paper deals with the application of deep learning methods to rotating machines fault
diagnosis. The main challenge is to design a fault diagnosis system connected with …

We present a novel method for real-time fault classification using the time history of acoustic
emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty …

Many technical applications, such as wind turbines, place high demands on journal
bearings regarding wear and reliability. The sequential start-stop operation as well as high …