Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
Large-scale wind turbine bearings including main bearings, gearbox bearings, generator
bearings, blade bearings and yaw bearings, are critical components for wind turbines to …
bearings, blade bearings and yaw bearings, are critical components for wind turbines to …
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 …
A hybrid deep-learning model for fault diagnosis of rolling bearings
Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …
applications. Deep learning is capable of producing high accuracy for bearing fault …
Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning
D He, C Liu, Z **, 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 …
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
The integration of large-scale wind power introduces issues in modern power systems
operations due to its strong randomness and volatility. These issues can be resolved via …
operations due to its strong randomness and volatility. These issues can be resolved via …
A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery
The mode number and mode frequency bandwidth control parameter (or quadratic penalty
term) have significant effects on the decomposition results of the variational mode …
term) have significant effects on the decomposition results of the variational mode …
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 …
Short-term wind speed prediction model based on GA-ANN improved by VMD
Y Zhang, G Pan, B Chen, J Han, Y Zhao, C Zhang - Renewable energy, 2020 - Elsevier
Wind power, as a potential new energy generation technology, is gradually develo**
towards to the mainstream energy in the world. However, the inherent random volatility of …
towards to the mainstream energy in the world. However, the inherent random volatility of …
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
The intermittent nature of wind can represent an obstacle to get reliable wind speed
forecasting, thus many methods were developed to improve the accuracy, due to unstable …
forecasting, thus many methods were developed to improve the accuracy, due to unstable …