Wind power is an important part of the energy transition, and the wind turbine gearbox is a core transmission component of wind power generation systems. Effective detection and identification of gearbox compound faults are therefore important for ensuring the stable and safe operation of wind turbines. This paper proposes an intelligent compound fault diagnosis method for wind turbine gearboxes based on fiber Bragg grating (FBG) vibration sensing and a ResNet model with efficient channel attention (ResNet-ECA). First, a flexure-hinged FBG accelerometer is designed and calibrated. The proposed sensor exhibits a flat sensitivity response from 5 Hz to 1000 Hz, a nominal sensitivity of 31.49 pm g−1, and linearity exceeding R2 = 0.999. Subsequently, vibration signals under eight different health states are collected using the FBG accelerometer. A Dragonfly algorithm-optimized variational mode decomposition method is applied to denoise the FBG wavelength-shift signals and obtain high-quality one-dimensional (1D) vibration data. The denoised 1D signals are then transformed into two-dimensional image representations using the Gramian angular summation field, and the resulting images are input into the ResNet-ECA model for compound fault identification. The results show that the proposed method achieves an exact-match accuracy of 95.83% and a macro-average F1-score of 0.9583. These results demonstrate the effectiveness of the proposed FBG sensing and intelligent diagnosis framework for compound fault diagnosis of wind turbine gearboxes.
Yuhang Peng, Xuetao Duan, Haoyuan Tian et al.· Measurement science and tech...· 0 citations
Wind power plays an important role in renewable energy generation, and the reliability of wind turbine gearboxes directly affects turbine operation and maintenance. Compound gear fault diagnosis remains challenging because multiple fault components may coexist and compound fault samples are often limited, leading to long-tailed data distributions. To address this problem, this study proposes a long-tailed multi-label diagnostic framework based on fiber Bragg grating (FBG) acceleration signals and multi-channel time-series imaging. Missing tooth, pitting, and tooth breakage faults are encoded as three independent labels to represent healthy, single-fault, double compound fault, and triple compound fault conditions. The one-dimensional FBG wavelength-shift signals are transformed into GASF-GADF-MTF three-channel images, which describe amplitude angular correlation, dynamic angular difference, and state transition information. A ResNet18-SE network trained with Focal Loss is developed to improve the recognition of minority compound fault samples. Experimental results show that the proposed method achieves an Exact Match Accuracy of 0.9950 and a Macro-F1 of 0.9980 on the Balanced dataset. Under the severe LT50 setting, it achieves an Exact Match Accuracy of 0.9739 and an F1123 of 0.9469. These results demonstrate the effectiveness of the proposed framework for FBG-based long-tailed compound fault diagnosis.
Yuhang Peng, Xuetao Duan, Haoyuan Tian et al.· Italian National Conference...· 0 citations
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