Establishment of Remaining Life Prediction Model for an Inertial Navigation System Based on Convolutional Neural Network and Filtering Fusion Algorithm
WANG Zhelan， ZHAO Hongjie， ZHAO Fan， SHEN Chenchen， WU Jiawei
Shanghai Spaceflight Precision Machinery Institute， Shanghai 201600， China
Abstract In the process of predicting the remaining life of key system components with a large amount of operational observation performance data in the product, it is difficult to establish the life distribution model due to the scarcity of life data, and traditional degradation process analysis models have poor adaptability of product performance observation data, which leads to low accuracy and weak validity of product life prediction. Fully excavating component degradation data information, based on relevant degradation analysis techniques and the filtering prediction method in the statistical model and the regression convolutional neural network prediction method in the machine learning technology, a fusion model of product remaining life prediction is established. The fusion model combines the filtering forecasting model’s ability to mine product degradation status, the ability to express uncertainty, and the data adaptability and forecasting accuracy of the regression convolutional neural network model, which improves the accuracy and effectiveness of product degradation data analysis, and can effectively predict the life of key product components, and provides auxiliary reference for health management of key system components with large amount of operational observation data in the product.
2023, Vol. 6 
