A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
A systematic review of fuzzy formalisms for bearing fault diagnosis
Bearings are fundamental mechanical components in rotary machines (engines, gearboxes,
generators, radars, turbines, etc.) that have been identified as one of the primary causes of …
generators, radars, turbines, etc.) that have been identified as one of the primary causes of …
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
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 …
data can be collected efficiently and economically, data-driven methods based on deep …
Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
In the recent years, deep learning-based intelligent fault diagnosis methods of rolling
bearings have been widely and successfully developed. However, the data-driven method …
bearings have been widely and successfully developed. However, the data-driven method …
Evolving deep echo state networks for intelligent fault diagnosis
Echo state network (ESN) is a fast recurrent neural network with remarkable generalization
performance for intelligent diagnosis of machinery faults. When dealing with high …
performance for intelligent diagnosis of machinery faults. When dealing with high …
Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process
X Yan, D She, Y Xu, M Jia - Knowledge-Based Systems, 2021 - Elsevier
The performance of complex rotor–bearing system usually decreases with the development
of the running time, which indicates that the rotor–bearing system usually goes through …
of the running time, which indicates that the rotor–bearing system usually goes through …
[HTML][HTML] Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion
M Huang, Z Liu, Y Tao - Simulation Modelling Practice and Theory, 2020 - Elsevier
Using multi-source sensing data based on the Internet of Things (IoT) with artificial
intelligence and big data processing technology to achieve predictive maintenance of …
intelligence and big data processing technology to achieve predictive maintenance of …
Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition
The forecast of electricity consumption plays an essential role in marketing management. In
this study, a random forest (RF) model coupled with ensemble empirical mode …
this study, a random forest (RF) model coupled with ensemble empirical mode …
Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor
Reciprocating compression machinery is the primary source of compressed air in the
industry. Undiagnosed faults in the machinery's components produce a high rate of …
industry. Undiagnosed faults in the machinery's components produce a high rate of …
Density peak clustering with connectivity estimation
W Guo, W Wang, S Zhao, Y Niu, Z Zhang… - Knowledge-Based Systems, 2022 - Elsevier
In 2014, a novel clustering algorithm called Density Peak Clustering (DPC) was proposed in
journal Science, which has received great attention in many fields due to its simplicity and …
journal Science, which has received great attention in many fields due to its simplicity and …