In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments.

As a low-carbon and clean energy source, Coalbed methane (CBM) is of great significance in reducing greenhouse gas emissions, optimizing the energy structure, safeguarding mine safety, and promoting the transformation to a green economy to achieve sustainable development. Coalbed methane (CBM) in Xinjiang’s steeply dipping coal seams is abundant but difficult to predict due to complex geology and distinct gas flow behaviors, making traditional methods ineffective. This study proposes GCN-BiGRU, a parallel dual-module model integrating seepage mechanics, reservoir engineering, geological structures, and production history. The GCN module models wells as nodes, using geological attributes and spatial distances to capture inter-well interference; the BiGRU module extracts temporal dependencies from production sequences. An adaptive fusion mechanism dynamically combines spatiotemporal features for robust prediction. Validated on Baiyanghe block data, the model achieved MAE 59.04, RMSE 94.25, and improved accuracy from 64.47% to 92.8% as training wells increased from 20 to 84. It also showed strong transferability to independent sub-regions, enabling real-time prediction and scenario analysis for CBM development and reservoir management.