Deep learning-based fault diagnosis methods have already attained remarkable
achievements in this field. However, rolling bearing frequently operates under variable …

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …

Despite the rapid development of deep learning-based intelligent fault diagnosis methods
on rotating machinery, the data-driven approach generally remains a'black box'to …

Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …

PHM technology is vital in industrial production and maintenance, identifying and predicting
potential equipment failures and damages. This enables proactive maintenance measures …

Condition monitoring and fault diagnosis of wind turbines is an attractive yet challenging
task. This paper presents a novel data-driven fault diagnosis scheme for wind turbines …

Fault diagnosis is important to the timely repair of the power transformer. However, machine
learning has not been exploited effectively for fault diagnosis due to the limitation of multi …

Recent advancements in deep learning methods bring computer-assistance a step closer to
fulfilling promises of safer surgical procedures. However, the generalizability of such …

With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

Federated learning is proposed as an alternative to centralized machine learning since its
client-server structure provides better privacy protection and scalability in real-world …