Fault diagnosis of mechanical equipment in high energy consumption industries in China: A review
Y Sun, J Wang, X Wang - Mechanical Systems and Signal Processing, 2023 - Elsevier
Building materials machinery equipment play an important role in the production of cement,
brick and tile, glass and other building materials, which are high energy consumption …
brick and tile, glass and other building materials, which are high energy consumption …
A review of current issues of marine current turbine blade fault detection
Marine current turbines (MCTs) has progressively attracted wider interest from the industry
and many research initiatives due to its potential as a novel world energy resource …
and many research initiatives due to its potential as a novel world energy resource …
Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction
MD Liu, L Ding, YL Bai - Energy Conversion and Management, 2021 - Elsevier
Wind speed is the key factor of wind power generation. With the increase of the proportion of
wind power generation in total power generation, the accurate prediction of wind speeds …
wind power generation in total power generation, the accurate prediction of wind speeds …
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 …
Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution
Z Wang, J Zhou, W Du, Y Lei, J Wang - Mechanical Systems and Signal …, 2022 - Elsevier
Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
A VMD and LSTM based hybrid model of load forecasting for power grid security
As the basis for the static security of the power grid, power load forecasting directly affects
the safety of grid operation, the rationality of grid planning, and the economy of supply …
the safety of grid operation, the rationality of grid planning, and the economy of supply …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines
To overcome current challenges in variational mode decomposition (VMD) and its variants
for the fault diagnosis of rotating machines, the decomposing characteristics of two sub …
for the fault diagnosis of rotating machines, the decomposing characteristics of two sub …
Two-phase deep learning model for short-term wind direction forecasting
Z Tang, G Zhao, T Ouyang - Renewable Energy, 2021 - Elsevier
Accurate and reliable wind direction prediction is important for improving wind power
conversion efficiency and operation safety. In this paper, a two-phase deep learning model …
conversion efficiency and operation safety. In this paper, a two-phase deep learning model …
Smart multichannel mode extraction for enhanced bearing fault diagnosis
In bearing fault diagnosis, multichannel data can contain more abundant and complete fault
information to alleviate the influence of accidental factors in a single channel. To fully …
information to alleviate the influence of accidental factors in a single channel. To fully …