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| 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 |
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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.
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Received: 29 June 2022
Published: 31 March 2023
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| [1] |
CAO Xiangang1,2(曹现刚)，LEI Zhuol*(雷卓)，LI Yanchuan1(李彦川), ZHANG Mengyuan1(张梦园),DUAN Xinyu1(段欣宇). Prediction Method of Equipment Remaining Life Based on Self-Attention Long Short-Term Memory Neural Network[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(5): 652-664. |
| [2] |
XIONG Yuefu (熊越夫), WU Xiaosheng∗ (吴校生), ZENG Zhaofeng (曾照丰),
HUANG Shan (黄山), CHEN Tianpei (陈天培). High Precision Temperature Measurement for Microfluidic Chip Applications[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 699-705. |
| [3] |
LE Jian, LIU Yichun, ZHANG Hua, CHEN Xiaoqi. A Detection Method of Wire Feeding Speed Based on Filtering Algorithm of Distortion Signal[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1334-1340. |
| [4] |
ZHANG Shuai, DING Liping, WANG Zhide, TANG Xiaofeng, FAN Wenjing. Research on Reliability of Power on Reset of CSD Serial Board in Launch Control Equipment[J]. Air & Space Defense, 2020, 3(2): 29-36. |
| [5] |
YUE Zhidong，KUAI Linping. Application of SolutionDiffusion Model in Removal of Cobalt Ions by Reverse Osmosis[J]. Journal of Shanghai Jiaotong University, 2014, 48(11): 1650-1654. |
| [6] |
HUANG Haiyanga,b,WANG Qingc，CHEN Jixiangb. An Empirical Analysis of Technology Innovation Diffusion in Chinese Universities Based on Bass Model[J]. Journal of Shanghai Jiaotong University, 2013, 47(07): 1164-1168. |
| [7] |
DU Gang1,JIANG Zhibin1,DIAO Xiaodi2,YAO Yang3
. Variances Handling for Clinical Pathway Based on Takagi-Sugeno FNNs with Random Cooperative Decomposing PSO Optimization
[J]. Journal of Shanghai Jiaotong University, 2010, 44(08): 1120-1124. |
|
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2023, Vol. 6 
