In the field of predictive maintenance (PdM), machine learning (ML) has gained importance
over the last years. Accompanying this development, an increasing number of papers use …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery
(RM) based on a novel continuous wavelet transform-local binary convolutional neural …

Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

Vibration signals are utilized widely for machinery fault diagnosis. These typical deep neural
networks (DNNs), eg, convolutional neural networks (CNNs) perform well in feature learning …

This paper deals with the development of a novel deep learning framework to achieve highly
accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional …

Despite deep learning models can largely release the pressure of manual feature
engineering in intelligent fault diagnosis of rotor-bearing systems, their performance mostly …

Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …

Data-driven intelligent diagnosis models expect to mine the health information of machines
from massive monitoring data. However, the size of faulty monitoring data collected in …