Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …

Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …

Numerous damage detection methods have been discovered to provide an early warning at
the earliest possible stage against structural damage or any type of abnormality in the wind …

Structural health monitoring for lightweight complex composite structures is being
investigated in this paper with a data-driven deep learning approach to facilitate automated …

Abstract—The fault diagnosis of wind turbines under nonstationary conditions is still
challenging. This paper proposes a novel tacho-less generalized demodulation (NTLGD) …

Damage detection and the classification of carbon fiber-reinforced composites using non-
destructive testing (NDT) techniques are of great importance. This paper applies an acoustic …

A damage detection method is presented which combines the random decrement technique
(RDT) with long and short-term memory (LSTM) networks. The method uses the measured …

The performance degradation assessment of offshore wind turbine (OWT) structures plays a
crucial role in ensuring the safe operation of the structures. This paper presents a method for …

Considering the complex influences of harsh marine environments, auxiliary structures, and
various operational processes, motion response of floating structures will inevitably exhibit …

Objective. The combination of the motor imagery (MI) electroencephalography (EEG) signals
and deep learning-based methods is an effective way to improve MI classification accuracy …