Abstract: [Objective] To achieve high-precision identification of mechanical faults in disconnectors, a parallel hybrid network incorporating attention mechanisms is proposed, which combines temporal and image features for intelligent diagnosis. [Methods] To fully exploit the feature information of dual-channel data, a bidirectional long short-term memory network was employed in the temporal channel to extract time-domain features from vibration signals, capturing the dynamic temporal variations of the signal and effectively reflecting the time-varying characteristics of mechanical faults. In the image channel, vibration signals were converted into two-dimensional images using Gramian angular fields, where polar coordinate mapping was utilized to preserve the temporal dynamics. A convolutional neural network was then used to extract key image features. Furthermore, a self-attention mechanism was introduced in the temporal channel and a channel attention mechanism in the image channel, enabling the model to adaptively adjust the weight of each channel, thereby emphasizing critical information and effectively reducing feature redundancy. [Results] Fault simulation experiments were conducted on GW4-126 type disconnectors, and vibration signals under four operating conditions were collected. The proposed method was compared with five other deep learning models. Experimental results demonstrated that the proposed method achieves a fault recognition accuracy exceeding 97%, effectively identifying typical mechanical faults such as mechanism jamming, looseness, and phase asynchrony. [Conclusion] The proposed parallel hybrid model overcomes the limitations of single-channel approaches by integrating two distinct types of feature information. The introduction of attention mechanisms enables the model to dynamically adjust weights, highlight salient features, and enhance the accuracy of fault identification. This method provides a reliable theoretical foundation and technical reference for the condition monitoring of disconnectors, holds significant potential for future fault diagnosis and equipment maintenance, and offers new insights for the development of smart grid technologies.