Recent years have witnessed the successful development of domain adaptation methods to
tackle cross-domain fault diagnosis problems. However, these methods require the target …

In the last decade, the adoption of technological tools in manufacturing industry, such as the
use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the …

Abstract Domain generalization methods can effectively identify machinery faults under
unseen new target working conditions. Nevertheless, most of them rely on data from multiple …

Remaining useful life (RUL) prediction plays a crucial role in bearing health management
which can guarantee the rotating machinery systems' safety and reliability. This paper …

In actual industrial scenarios, the centralized learning paradigm for remaining useful life
(RUL) prediction of rotating machineries usually suffers from several bottlenecks. Firstly, the …

Remaining useful life (RUL) prediction is an essential task in ensuring reliability in intelligent
manufacturing. Recent advances in deep learning-based data-driven methods have shown …

Knowledge and data dual-driven (KDDD) represents a novel paradigm that leverages the
strengths of data-driven methods in feature representation and knowledge transfer, while …

Featured Application The proposed robust health prognostics technique identifies outer race
bearing failures and predicts the remaining useful lifetimes of the bearings of electric motors …

Federated learning ensures the privacy of fault diagnosis by maintaining a decentralized
and local training data approach, eliminating the need to share confidential information with …

Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee
safety and reliability of mechanical systems. Due to the variability of machinery working …