Jan Helsen

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study

Digital Twin: Benefits, use cases, challenges, and opportunities

Artificial intelligence, machine learning and deep learning in advanced robotics, a review

A novel hybrid decision making approach for the strategic selection of wind energy projects

Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Short-term wind speed prediction model based on GA-ANN improved by VMD

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Applications of machine learning to machine fault diagnosis: A review and roadmap

Machine learning methods for wind turbine condition monitoring: A review

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Long-Term Monitoring of Wind Farms Using Big Data Approach

Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy

A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection

MiCLAD as a platform for real-time monitoring and machine learning in laser metal deposition

Experimental investigation of bearing slip in a wind turbine gearbox during a transient grid loss event