A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
[HTML][HTML] Machine learning methods for wind turbine condition monitoring: A review
This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …
used for condition monitoring in wind turbines (eg blade fault detection or generator …
Data-driven methods for predictive maintenance of industrial equipment: A survey
W Zhang, D Yang, H Wang - IEEE systems journal, 2019 - ieeexplore.ieee.org
With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on
data-driven methods has become the most effective solution to address smart manufacturing …
data-driven methods has become the most effective solution to address smart manufacturing …
A review on the application of deep learning in system health management
Given the advancements in modern technological capabilities, having an integrated health
management and diagnostic strategy becomes an important part of a system's operational …
management and diagnostic strategy becomes an important part of a system's operational …
Monitoring and identifying wind turbine generator bearing faults using deep belief network and EWMA control charts
H Li, J Deng, S Yuan, P Feng… - Frontiers in Energy …, 2021 - frontiersin.org
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic
and random stress imposed on the generator bearing of a wind turbine may lead to …
and random stress imposed on the generator bearing of a wind turbine may lead to …
A comprehensive review on signal-based and model-based condition monitoring of wind turbines: Fault diagnosis and lifetime prognosis
Wind turbines play an increasingly important role in renewable power generation. To ensure
the efficient production and financial viability of wind power, it is crucial to maintain wind …
the efficient production and financial viability of wind power, it is crucial to maintain wind …
Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks
Gearbox fault diagnosis is expected to significantly improve the reliability, safety and
efficiency of power transmission systems. However, planetary gearbox fault diagnosis …
efficiency of power transmission systems. However, planetary gearbox fault diagnosis …
Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset
EF Swana, W Doorsamy, P Bokoro - Sensors, 2022 - mdpi.com
Data-driven methods have prominently featured in the progressive research and
development of modern condition monitoring systems for electrical machines. These …
development of modern condition monitoring systems for electrical machines. These …
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature …
M Fernandes, JM Corchado, G Marreiros - Applied Intelligence, 2022 - Springer
When put into practice in the real world, predictive maintenance presents a set of challenges
for fault detection and prognosis that are often overlooked in studies validated with data from …
for fault detection and prognosis that are often overlooked in studies validated with data from …