scholarly journals Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019 ◽  
Vol 9 (15) ◽  
pp. 3017
Author(s):  
Qian Zhao ◽  
Xiang Jia ◽  
Zhijun Cheng ◽  
Bo Guo
Keyword(s):  
Information Fusion ◽  
Bayesian Method ◽  
Residual Life ◽  
Small Sample ◽  
Similar Data ◽  
Posterior Distributions ◽  
Source Information ◽  
Monte Carlo Simulation Study ◽  
Life Estimation ◽  
Distributed Components

Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

A Novelty Model for Reliability Assessment of Complex System

2014 ◽  
Vol 571-572 ◽  
pp. 118-123
Author(s):  
Zong Run Yin ◽  
Dong Su ◽  
Jun Shan Li
Keyword(s):  
Complex System ◽  
Information Fusion ◽  
Bayesian Method ◽  
Reliability Assessment ◽  
Reliability Model ◽  
Source Information ◽  
Component Reliability ◽  
Reliability Parameters ◽  
System A ◽  
Go Methodology

Aiming at the difficulty in reliability assessment of complex system. A novelty model based on Bayesian method and GO methodology is proposed. Bayesian method is adopted for multi-source information fusion to build the component reliability model, and then GO methodology is utilized to integrate the component reliability parameters and form the reliability model of the system. At last, an instance of reliability assessment for complex electronic equipment is given to show the effectiveness of the model. Result shows that, this method take advantage of Bayesian method and GO methodology, it provide useful reference for relative applications.

scholarly journals Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

2019 ◽  
Vol 9 (2) ◽  
pp. 300
Author(s):  
Jianyong Zuo ◽  
Jingxian Ding ◽  
Furen Feng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Small Sample ◽  
Support Vector ◽  
Ratio Test ◽  
Fusion Method ◽  
Source Information ◽  
Braking System ◽  
Chinese Standard ◽  
Multiple Units

To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman's state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions.

Research on Reliability Assessment for the Armored Equipment Based on Multi-Source Information Fusion

2013 ◽  
Vol 760-762 ◽  
pp. 2091-2094
Author(s):  
Jian Du ◽  
Bao Jun Fei ◽  
Ying Liu ◽  
Guo Zheng Yao
Keyword(s):  
Field Test ◽  
Test Data ◽  
Information Fusion ◽  
Reliability Assessment ◽  
Small Sample ◽  
Test Results ◽  
Fuzzy Information ◽  
Source Information ◽  
Sample Test ◽  
Fusion Theory

In order to solve the problem of reliability evaluation for armored equipment, used Bayes fusion theory, combined with minimal amounts of reliability information on the field test, the paper fusioned reliability information of the same model for armored equipment, and completed the reliability assessment for a certain type of armored equipment. The test results show that the technology of multi-source information fusion can effectively solve the fusion problem of the prior fuzzy information and small sample test data, improve the accuracy of the reliability assessment, prove the feasibility and effectiveness for the multi-source information fusion in the armored equipment assessment.

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