Robust Bearing Fault Diagnosis Using Transfer Learning and SISA-Based Machine Unlearning

Mlamsan-based cross-case fault diagnosis of rotor-bearing systems

A multi-scale linear attention multi-source subdomain adaptation network (MLAMSAN) that integrates the multi-scale linear attention (MLA) that can achieve fault diagnosis under cross operating conditions through subdomain feature alignments that exhibits the superior diagnostic performance and the strong generalization ability.

BEARING EARLY FAULT DETECTION AND REMAINING USEFUL LIFE ESTIMATION WITH KNOWLEDGE-INFORMED MACHINE LEARNING

Hybrid Machine Learning Framework for SCADA-Based Anomaly Detection in Wind Turbine Systems Using Ensemble Learning

This paper proposes a Hybrid Machine Learning Framework for SCADA-based anomaly detection in wind turbine systems using ensemble learning, which significantly outperforms standalone approaches and substantially reduces false negative predictions.

A reliable rolling bearing fault diagnosis method based on Titan

TitanDiag, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts by adapting this mechanism to fault diagnosis for rolling bearings.

A fault diagnosis method for pilot-operated solenoid valves based on CRQA-Stacking integrated learning

A fault diagnosis model for pilot-operated solenoid valves based on cross-recurrence quantification analysis (CRQA) and stacking heterogeneous ensemble learning is proposed, achieving high accuracy and stability in fault diagnosis for sparse and imbalanced fault samples.

Multi-Class Fault Detection and Diagnosis of Rolling Bearings: a Machine Learning Approach

Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.