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Open access 2026

Prediction and Classification of Residual Service Life in Wind Turbine Bearings Under Variable Speed Conditions Using Hybrid Machine Learning Models

A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maint...

Gustavo Gomes Do Valle, Benjamin Soudhan, Meisam Mahdavi et al. · 0 citations
Open access Aug 2026

Optimization and Improvement of Operation and Maintenance Efficiency of Condition Monitoring Technology for Offshore Wind Turbines

This study proposes an integrated condition-monitoring and predictive-maintenance framework for offshore wind turbines operating in harsh marine environments. To address the challenges of signal degradation, environmental interference, and limited fault-warning capability, a multi-source sensing architecture is develop...

Yanqing Ouyang, W. Liang · 0 citations
Conference Open access Aug 2026

A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technolo...

Juhi Padma, Hemant J. Sagar · 0 citations
Aug 2026

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.

M. I. Quamar, Abdulrazaq Nafiu Abubakar, Ali Nasir · 0 citations
Open access Sep 2026

A fault diagnosis method using frequency-aware adaptive domain alignment for bearings

In practical applications of rolling bearings, variations in the measurement data distribution caused by diverse operating conditions result in complicated domain adaptation tasks and significantly impair the effectiveness and generalizability of conventional models. Therefore, this study proposes a frequency-domain-aw...

Shu-Hao Wang, Peng Shen, Ming-Kai Wang et al. · 0 citations
Conference Aug 2026

Fault Diagnosis Method for Rolling Bearings Based on Wavelet Packet Analysis and Adaboost.M2 Algorithm

Aiming at the precise diagnosis requirements for multiple types of rolling bearing faults and reducing the impact of bearing faults on the operational performance of mechanical equipment, an intelligent fault diagnosis method combining wavelet packet transform (WPT) energy feature extraction and AdaBoost.M2 is proposed...

Xiao-Xuan Jiao, Xin Tao, Wen-Bo Zhang et al. · 0 citations

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