[HTML][HTML] The advance of digital twin for predictive maintenance: The role and function of machine learning
The recent advance of digital twin (DT) has greatly facilitated the development of predictive
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …
Machine learning applications in health monitoring of renewable energy systems
Rapidly evolving renewable energy generation technologies and the ever-increasing scale
of renewable energy installations are driving the need for more accurate, faster, and smarter …
of renewable energy installations are driving the need for more accurate, faster, and smarter …
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …
exposed to challenging operational environments. Monitoring and diagnosing potential …
Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform
S Tang, Y Zhu, S Yuan - Reliability Engineering & System Safety, 2022 - Elsevier
Hydraulic piston pump is known as one of the most critical parts in a typical hydraulic
transmission system. It is imperative to probe into an accurate fault diagnosis method to …
transmission system. It is imperative to probe into an accurate fault diagnosis method to …
[HTML][HTML] An outliers detection and elimination framework in classification task of data mining
An outlier is a datum that is far from other data points in which it occurs. It can have a
considerable impact on the output. Therefore, removing or resolving it before the analysis is …
considerable impact on the output. Therefore, removing or resolving it before the analysis is …
Fault detection of wind turbines using SCADA data and genetic algorithm-based ensemble learning
Due to global efforts to reduce the rise in the average global temperature by replacing fossil
fuels, the amount of wind power installed worldwide is continuously increasing. The costs …
fuels, the amount of wind power installed worldwide is continuously increasing. The costs …
[PDF][PDF] AI-Enhanced lifecycle assessment of renewable energy systems
KE Bassey, AR Juliet, AO Stephen - Engineering Science & …, 2024 - researchgate.net
Bassey, Juliet, & Stephen, P. No. 2082-2099 Page 2083 accuracy. Key findings demonstrate
that AI-enhanced LCA models significantly improve the precision and depth of …
that AI-enhanced LCA models significantly improve the precision and depth of …
Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions
Intelligent fault diagnosis based on domain adaptation has recently been extensively
researched to promote reliability of safety-critical assets under different working conditions …
researched to promote reliability of safety-critical assets under different working conditions …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions
Y Shi, A Deng, M Deng, M Xu, Y Liu, X Ding… - Reliability Engineering & …, 2023 - Elsevier
Recent years have witnessed the successful development of domain adaptation methods to
tackle cross-domain fault diagnosis problems. However, these methods require the target …
tackle cross-domain fault diagnosis problems. However, these methods require the target …