Despite the recent success of deep-learning-based fault diagnosis of rotating machinery, to
enable accurate and robust diagnosis models, existing approaches proceed with the …

This paper proposes a scalable and unsupervised feature engineering method that uses
vibration imaging and deep learning. For scalability, a vibration imaging approach is …

Failures of engineered systems can result in enormous repair/replacement costs and can
also cause life-threatening consequences, such as explosion and fire. Since the 1980s …

Fault diagnosis of compressors in air conditioners is challenging owing to the imbalance
and nonlinearity of the vibration data because of the contrasting failure modes. This study …

Fault diagnosis of rotor systems is important to prevent unexpected failures. Recently, deep
learning (DL) methods, such as a convolutional neural network (CNN), have been utilized in …

Deep learning-based research has drawn much attention in the field of fault diagnosis of
various mechanical systems due to its powerful performance. In deep learning-based …

Significant efforts have been recently devoted to the qualitative and quantitative evaluation
of resilience in engineering systems. Current resilience evaluation methods, however, have …

Some anomaly states of journal bearing rotor systems are direction-oriented (eg, rubbing,
misalignment). In these situations, vibration signals vary according to the direction of the …

The process of validating newly-defined state observers can potentially require a significant
amount of data gathered from instrumentation. However, collecting data for high-rate …

Relevant classification of the stationary operating conditions of wind turbines (WTs) aids in
the selection of an optimal condition monitoring technique. This paper presents a general …