Wavelet transform application for/in non-stationary time-series analysis: A review
Non-stationary time series (TS) analysis has gained an explosive interest over the recent
decades in different applied sciences. In fact, several decomposition methods were …
decades in different applied sciences. In fact, several decomposition methods were …
Recent progress of chatter prediction, detection and suppression in milling
L Zhu, C Liu - Mechanical Systems and Signal Processing, 2020 - Elsevier
Machining chatter has been studied by scholars over the past decades, since chatter has a
significant impact on surface quality and productivity. Researchers have carried out …
significant impact on surface quality and productivity. Researchers have carried out …
Wavelet transform for rotary machine fault diagnosis: 10 years revisited
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
Effective energy consumption forecasting using empirical wavelet transform and long short-term memory
L Peng, L Wang, D Xia, Q Gao - energy, 2022 - Elsevier
Energy consumption is an important issue of global concern. Accurate energy consumption
forecasting can help balance energy demand and energy production. Although there are …
forecasting can help balance energy demand and energy production. Although there are …
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
KU Jaseena, BC Kovoor - Energy Conversion and Management, 2021 - Elsevier
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …
renewable energy resources. Wind energy is attracting attention worldwide due to its …
A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis
Being an effective methodology to adaptatively decompose a multi-component signal into a
series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited …
series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited …
Feature mode decomposition: New decomposition theory for rotating machinery fault diagnosis
Y Miao, B Zhang, C Li, J Lin… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored
for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose …
for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose …
Loading condition monitoring of high-strength bolt connections based on physics-guided deep learning of acoustic emission data
Aiming at life-cycle condition monitoring of high-strength bolt connections, a physics-guided
deep learning framework integrating supervised and unsupervised learning was developed …
deep learning framework integrating supervised and unsupervised learning was developed …
Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks
Recently, the boom in wind power industry has called for the accurate and stable wind
speed forecasting, on which reliable wind power generation systems depend heavily. Due to …
speed forecasting, on which reliable wind power generation systems depend heavily. Due to …
Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning
D He, C Liu, Z Jin, R Ma, Y Chen, S Shan - Energy, 2022 - Elsevier
Flywheel energy storage system is widely used in train braking energy recovery, and has
achieved excellent energy-saving effect. As a key component of the flywheel energy storage …
achieved excellent energy-saving effect. As a key component of the flywheel energy storage …