Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …

Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …

We propose a one-class neural network (OC-NN) model to detect anomalies in complex
data sets. OC-NN combines the ability of deep networks to extract a progressively rich …

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is
challenging but necessary to ensure safety and economical operation in industries …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate
condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to …