The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge
amount of data, which is scarcely available in industry. This work shows that convolutional …

Classical machine learning approaches have made remarkable contributions to the field of
data-driven techniques for bearing fault diagnosis. However, these algorithms mainly …

Bearing is one of the most critical components in most electrical and power drives. Effective
bearing fault diagnosis is important for keeping the electrical and power drives safe and …

Roller bearings form key components in many machines and, as such, their health status
can directly influence the operation of the entire machine. Acoustic signals collected from …

Bearing fault diagnosis is a serious problem on which researchers have focused to ensure
the reliability and availability of rotating machinery. Knowledge-based methods are capable …

Recent convolutional neural network (CNN) models in image processing can be used as
feature-extraction methods to achieve high accuracy as well as automatic processing in …

The increased presence of advanced sensors on the production floors has led to the
collection of datasets that can provide significant insights into machine health. An important …

Vibration signal processing is a crucial task in machine fault diagnosis. Several signal
processing methods in the past relied on more conventional approaches to diagnose …

Diagnosis of bearing faults in real-time is challenging when healthy bearing conditions are
mixed with faulty ones, affecting the overall system of rotating machinery. Deep Learning …

Bearing defects are a common problem in rotating machines and equipment that can lead to
unexpected downtime, costly repairs, and even safety hazards. Diagnosing bearing defects …