A novel diagnosis methodology of gear oil for wind turbine combining stepwise multivariate regression and clustered federated learning framework

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Data-driven approaches demonstrate significant potential in accurately diagnosing faults wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) to gear oil diagnosis. Initially, stepwise multivariate regression (SMR) model is introduced and optimized after data process, which integrates multiscale feature AIC diagnosis feature. Subsequently, tackle heterogeneity among different indicators, canonical correlation series of representations are extracted from the SMR models, combining CFLF method proposed assess performance oil. Actual analysis turbine showcase superior over single with higher prediction accuracy 35.73%. This study provides new technique for evaluating energy sector.

Language: Английский

A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2712 - 2712

Published: April 25, 2025

This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and 1D convolutional residual network (1D conv ResNet). effectively captures both spatial temporal dependencies, enhances noise resilience, extracts discriminative features from nonstationary nonlinear vibration signals. is initially trained on controlled laboratory dataset further validated real artificial subsets of the Paderborn dataset, demonstrating strong generalization across diverse conditions. t-SNE visualizations confirm clear separability between categories, supporting model’s capability precise reliable potential real-time predictive maintenance in complex industrial environments.

Language: Английский

Improved residual attention convolutional neural network for rotating machinery fault diagnosis in the presence of strong noise

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(6)

Published: May 1, 2025

Language: Английский