Method for Predicting the Residual Life of Weaponry based on Transformer Bi-LSTM Modell
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 equipment operation status and the complex fault location process,it results in lower efficiency of traditional maintenance methods.The equipment usage data has the characteristics of continuity,long-term,and instability,and some deep learning models cannot deal with the historical dependence and association of the degraded states.The remaining life prediction architecture at the component level is built to study the feature engineering,degradation index construction and Transformer bi-directional long short-term memory(Bi-LSTM)model.The distance coding is used to realize the technological innovation of the deep learning model and optimize the prediction effect of the model.Based on normal sample data of primary components of certain weapons and equipment,this method analyzes and validates the remaining life of the device,it can be effectively and accurately predicted in operation with 90％of the designed test life.The proposed method meets the application re-quirements of early warning and replacement for weaponry components,ensuring weaponry readiness.
weaponryresidual life predictionhealth managementTransformerBi-LSTMdegradation indicatorsdistance coding
NETL
NSTL
万方数据
