Vibration signal-based early fault prognosis: Status quo and applications
Abstract To implement Prognostics and Health Management (PHM) for industrial systems, it
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
A review of early fault diagnosis approaches and their applications in rotating machinery
Y Wei, Y Li, M Xu, W Huang - Entropy, 2019 - mdpi.com
Rotating machinery is widely applied in various types of industrial applications. As a
promising field for reliability of modern industrial systems, early fault diagnosis (EFD) …
promising field for reliability of modern industrial systems, early fault diagnosis (EFD) …
Fine-tuning transfer learning based on DCGAN integrated with self-attention and spectral normalization for bearing fault diagnosis
In the current big-data context of Industry 4.0, insufficient training data has become a major
bottleneck in develo** data-driven diagnosis approaches, restricting the accuracy of deep …
bottleneck in develo** data-driven diagnosis approaches, restricting the accuracy of deep …
Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network
The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …
bearing fault classification accuracy using deep learning models. However, implementing a …
Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders
H Liu, J Zhou, Y Zheng, W Jiang, Y Zhang - ISA transactions, 2018 - Elsevier
As the rolling bearings being the key part of rotary machine, its healthy condition is quite
important for safety production. Fault diagnosis of rolling bearing has been research focus …
important for safety production. Fault diagnosis of rolling bearing has been research focus …
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …
practical importance. For this purpose, ensemble transfer convolutional neural networks …
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 …
Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal
Structural health monitoring (SHM) of bolted joints has played a vital role in estimation of bolt
looseness and prediction of residual service life of bolted connections, thus saving money …
looseness and prediction of residual service life of bolted connections, thus saving money …
An ensemble dynamic self-learning model for multiscale carbon price forecasting
Precise carbon price forecasting can provide decision support for policy-makers and
investors. However, due to the high non-stationarity and nonlinearity of carbon price series …
investors. However, due to the high non-stationarity and nonlinearity of carbon price series …
Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity
X Yan, Y Liu, Y Xu, M Jia - Renewable Energy, 2021 - Elsevier
When wind turbine driving system (WTDS) undergoes abnormal conditions, the fault
information hidden in WTDS scatters over multiple signal channels and hence inadequate …
information hidden in WTDS scatters over multiple signal channels and hence inadequate …