A Fault Diagnosis Method for Roadheader Cutting Head Based on GADF and Attention-Enhanced Transfer Learning AlexNet

The cutting head is the primary cutting load device of the roadheader. The harsh, complex excavation environment often leads to a scarcity of labelled fault samples, significantly hindering the development of accurate fault diagnosis models. This paper proposes a novel fault diagnosis method based on the Gramian angular difference field (GADF) and an attention-enhanced transfer-learning AlexNet. The collected one-dimensional vibration signals are transformed into two-dimensional image data using GADF to capture transient impact gradients and preserve absolute temporal correlations. To overcome data limitations, the method retains the base convolutional feature extractor of an AlexNet model pre-trained on ImageNet, whilst discarding the original fully connected and classification layers. A novel classification head incorporating a multihead self-attention (MSA) mechanism is constructed to fine-tune the network specifically for the fault diagnosis task. This structural modification adaptively assigns higher weights to fault-sensitive spatial regions, significantly enhancing the model’s feature extraction focus and generalisation capability even under intense background noise. Experimental validation was conducted on a scaled cutting head fault diagnosis test bench. The results demonstrate that the proposed method outperforms other baselines across evaluation metrics, exhibiting robust recognition accuracy and stability. This effectively identifies the cutting head’s operating condition, offering a novel and practical approach for future underground fault diagnosis in coal mines.