Title
Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion
Abstract
AbstractAn asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor.
Year
DOI
Venue
2017
10.1155/2017/4139563
Periodicals
Field
DocType
Volume
Wiener process,Data mining,Asynchronous communication,Control theory,Algorithm,Fusion,Kalman filter,Probability distribution,Mathematics,Recursion,Genetic algorithm,Maximization
Journal
2017
Issue
ISSN
Citations 
1
1687-5249
0
PageRank 
References 
Authors
0.34
15
4
Name
Order
Citations
PageRank
Yanyan Hu1112.59
Shuai Qi200.34
Xiaoling Xue320.73
Kaixiang Peng45312.22