Neural networks employed for fault diagnosis in complex scenarios often face challenges like strong noise interference and incomplete information from individual sensors, leading to degraded diagnostic performance. To address this issue, a cross-sensor convolutional neural network with dual residual and feature adaptation model is proposed. Firstly, during the data feature extraction process, a dual-ring residual module is utilized to alleviate the gradient vanishing problem during training. Subsequently, the convolutional block attention module attention mechanism is introduced to enhance the model's ability to focus on critical features. Then, a feature optimization and reconstruction module is utilized to improve the efficiency of feature learning and the capability of feature expression. Thereafter, an adaptive feature fusion module is employed to adaptively fuse high-level features extracted from different sensors. Finally, the fused features are classified through a global average pooling layer, a fully connected layer, and a Softmax function to accomplish the fault diagnosis task. The results demonstrate that the proposed method effectively integrates multi-sensor data features and exhibits robustness against noise of varying intensities. The average diagnostic accuracy of the model reaches 97.48% under noise levels ranging from -2 to -18 dB, showing an improvement of 1.88% compared to using a single sensor. This study provides an effective reference for solving fault diagnosis problems in complex scenarios.