Abstract: Relying solely on single sensor data can only obtain partial and one-sided operational information of equipment, and the reliability of large-scale equipment fault maintenance is low. Therefore, a large-scale equipment fault predictive maintenance method based on digital twin and multimodal deep learning is proposed. Sort out the basic information of equipment and deploy multiple types of sensors to collect data. Adopt a hierarchical modeling strategy to construct a four level digital twin model of "geometric layer physical layer behavioral layer decision layer", achieving precise mapping and closed-loop linkage between physical equipment and virtual models; Preprocess the collected multimodal data, extract single modal features, and combine attention mechanism to complete multimodal feature fusion, obtaining fusion features that can comprehensively reflect the equipment status; Based on the fusion of features and digital twin model, BiLSTM model is adopted and combined with health index to predict the remaining service life of equipment. Warning thresholds are set to achieve fault warning and complete predictive maintenance of equipment. Experiments have shown that the research method reduces the vibration signal MAE to 0.032 and RMSE to 0.045 under no-load conditions, and the temperature signal MAE to 0.028 and RMSE to 0.039, resulting in a reduction rate of 60% -85% in unplanned shutdowns for most equipment and improving the reliability of large-scale equipment fault maintenance.