Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples
Nonstationary signal analysis is one of the main topics in the field of machinery fault
diagnosis. Time–frequency analysis can identify the signal frequency components, reveals …
diagnosis. Time–frequency analysis can identify the signal frequency components, reveals …
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) …
Short term load forecasting based on feature extraction and improved general regression neural network model
Y Liang, D Niu, WC Hong - Energy, 2019 - Elsevier
Along with the deregulation of electric power market as well as aggregation of renewable
resources, short term load forecasting (STLF) has become more and more momentous …
resources, short term load forecasting (STLF) has become more and more momentous …
New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms
Abstract Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering
(IF) are largely implemented for representing a signal as superposition of simpler well …
(IF) are largely implemented for representing a signal as superposition of simpler well …
Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition
Hydrological time series forecasting is one of the most important applications in modern
hydrology, especially for effective reservoir management. In this research, the auto …
hydrology, especially for effective reservoir management. In this research, the auto …
Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
Z Li, J Chen, Y Zi, J Pan - Mechanical systems and signal processing, 2017 - Elsevier
As one of most critical component of high-speed locomotive, wheel set bearing fault
identification has attracted an increasing attention in recent years. However, non-stationary …
identification has attracted an increasing attention in recent years. However, non-stationary …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
A robust method for non-stationary streamflow prediction based on improved EMD-SVM model
E Meng, S Huang, Q Huang, W Fang, L Wu, L Wang - Journal of hydrology, 2019 - Elsevier
Monthly streamflow prediction can offer important information for optimal management of
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …
Ensemble empirical mode decomposition: a noise-assisted data analysis method
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach
consists of sifting an ensemble of white noise-added signal (data) and treats the mean as …
consists of sifting an ensemble of white noise-added signal (data) and treats the mean as …
Multivariate empirical mode decomposition
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-
frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are …
frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are …