GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS

Yang Fulong; Gong Lijun; Wu Feng; Zhou Jinjin; Yang Qing; Zhang Teng

doi:10.19912/j.0254-0096.tynxb.2025-0147

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 361-371. DOI: 10.19912/j.0254-0096.tynxb.2025-0147

GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS

Yang Fulong^1,2, Gong Lijun¹, Wu Feng¹, Zhou Jinjin¹, Yang Qing¹, Zhang Teng¹

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Abstract

A fault diagnosis method for wind turbine rolling bearings is proposed, which combines the gradient dynamic selective alignment （DSA） method, dual-channel feature fusion technique （DCFF）, and deep learning. Firstly, the acquired one-dimensional time series data are transformed into two different two-dimensional image representations using recursive mapping （RP） and Markov transfer field （MTF）. These transformed images are then input into an encoder for reconstruction into a low-dimensional representation. Next, a dual-channel feature fusion convolutional neural network （DCFFC） is constructed, embedding an improved attention mechanism within FRNet to extract in-depth feature information. A second-level feature fusion is performed to complete the feature classification. Finally, a gradient masking matrix is constructed using the data reconstruction loss of the decoder and the feature extraction network classification loss to achieve gradient dynamic self-adaptation throughout the fault diagnosis process. Experimental validation using the Paderborn dataset demonstrates that the proposed method achieves a diagnostic accuracy of 99%, effectively extracting fault-specific diagnostic information.

Key words

fault diagnosis / rolling bearing / wind power generator / convolutional neural network / gradient adaptive

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Yang Fulong, Gong Lijun, Wu Feng, Zhou Jinjin, Yang Qing, Zhang Teng. GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 361-371 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0147

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