Applications of machine learning to machine fault diagnosis: A review and roadmap
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …
machine fault diagnosis. This is a promising way to release the contribution from human …
Multistability phenomenon in signal processing, energy harvesting, composite structures, and metamaterials: A review
Multistability is the phenomenon of multiple coexistent stable states, which are highly
sensitive to perturbations, initial conditions, system parameters, etc. Multistability has been …
sensitive to perturbations, initial conditions, system parameters, etc. Multistability has been …
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Uninterrupted and trouble-free operation of induction motors (IMs) is the compulsion of the
modern industries. Firstly, the paper reviews the conventional time and spectrum signal …
modern industries. Firstly, the paper reviews the conventional time and spectrum signal …
A survey on deep learning based bearing fault diagnosis
DT Hoang, HJ Kang - Neurocomputing, 2019 - Elsevier
Abstract Nowadays, Deep Learning is the most attractive research trend in the area of
Machine Learning. With the ability of learning features from raw data by deep architectures …
Machine Learning. With the ability of learning features from raw data by deep architectures …
Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals
RFR Junior, IA dos Santos Areias, MM Campos… - Measurement, 2022 - Elsevier
Fault detection and diagnosis in time series data are becoming mainstream in most
industrial applications since the increase of monitoring sensors in machinery. Traditional …
industrial applications since the increase of monitoring sensors in machinery. Traditional …
A survey of artificial neural network in wind energy systems
Wind energy has become one of the most important forms of renewable energy. Wind
energy conversion systems are more sophisticated and new approaches are required based …
energy conversion systems are more sophisticated and new approaches are required based …
Rolling element bearing fault diagnosis using convolutional neural network and vibration image
DT Hoang, HJ Kang - Cognitive Systems Research, 2019 - Elsevier
Detecting in prior bearing faults is an essential task of machine health monitoring because
bearings are the vital components of rotary machines. The performance of traditional …
bearings are the vital components of rotary machines. The performance of traditional …
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
H Shao, H Jiang, H Zhang, W Duan, T Liang… - Mechanical systems and …, 2018 - Elsevier
The vibration signals collected from rolling bearing are usually complex and non-stationary
with heavy background noise. Therefore, it is a great challenge to efficiently learn the …
with heavy background noise. Therefore, it is a great challenge to efficiently learn the …
Gated dual attention unit neural networks for remaining useful life prediction of rolling bearings
In the mechatronic system, rolling bearing is a frequently used mechanical part, and its
failure may result in serious accident and major economic loss. Therefore, the remaining …
failure may result in serious accident and major economic loss. Therefore, the remaining …
A review of wind turbine bearing condition monitoring: State of the art and challenges
HDM de Azevedo, AM Araújo… - … and Sustainable Energy …, 2016 - Elsevier
Since the early 1980s, wind power technology has experienced an immense growth with
respect to both the turbine size and market share. As the demand for large-scale wind …
respect to both the turbine size and market share. As the demand for large-scale wind …