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Weaponry is responsible for the important mission of safeguarding national security, and its stable operation is of great nation defense and political significance. Due to the inconvenient interruption of the operation status of the equipment and the complex fault location process, the traditional maintenance method is inefficient. The equipment usage data is continuous, long-term, and instability, and some deep learning models cannot deal with the historical dependence and association of degraded states. By constructing the remaining life prediction architecture at the component level, the feature engineering, degradation index construction and Transformer-Bi-LSTM model are studied, and distance coding are used to realize the technological innovation of the deep learning model and optimize the prediction effect of the model. Based on the normal sample data of the primary components of a weapon equipment, this method has been analyzed and validated. It can effectively and accurately predict the remaining life when the device has been in operation for 90% of its designated test life span. The proposed method meets the requirements for early warning and replacement reminders for weapon equipment devices, ensuring equipment combat readiness integrity.