Parameter estimation for partially observable systems subject to random failure

2012 ◽  
Vol 29 (3) ◽  
pp. 279-294 ◽  
Author(s):  
Michael Jong Kim ◽  
Viliam Makis ◽  
Rui Jiang
Keyword(s):  
Parameter Estimation ◽  
Partially Observable Systems ◽  
Random Failure ◽  
Partially Observable

On-line parameter estimation for a failure-prone system subject to condition monitoring

2004 ◽  
Vol 41 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Parameter Estimation ◽  
Condition Monitoring ◽  
The State ◽  
Homogeneous Markov Process ◽  
Line Parameter ◽  
Failure State ◽  
Finite State ◽  
Random Failure ◽  
On Line ◽  
Partially Observable System

In this paper, we study the on-line parameter estimation problem for a partially observable system subject to deterioration and random failure. The state of the system evolves according to a continuous-time homogeneous Markov process with a finite state space. The state of the system is hidden except for the failure state. When the system is operating, only the information obtained by condition monitoring, which is related to the working state of the system, is available. The condition monitoring observations are assumed to be in continuous range, so that no discretization is required. A recursive maximum likelihood (RML) algorithm is proposed for the on-line parameter estimation of the model. The new RML algorithm proposed in the paper is superior to other RML algorithms in the literature in that no projection is needed and no calculation of the gradient on the surface of the constraint manifolds is required. A numerical example is provided to illustrate the algorithm.

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