Stochastic Filtering

2015 ◽  
pp. 1396-1399
Author(s):  
M. V Tretyakov
Keyword(s):  
Stochastic Filtering

Stochastic Filtering and Control.

1977 ◽  
Author(s):  
Tyrone E. Duncan
Keyword(s):  
Stochastic Filtering ◽  
And Control

Multi-Sample Stochastic Filtering of OCT Skin Images

2002 ◽  
Vol 2 (1-2) ◽  
pp. 117-124
Author(s):  
P.G. Akishin ◽  
E.P. Akishina ◽  
P. Akritas ◽  
I. Antoniou ◽  
J. Ioannovich ◽  
...  
Keyword(s):  
Stochastic Filtering

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Zhang ◽  
Anchen Wang
Keyword(s):  
Life Prediction ◽  
Engineering Application ◽  
Rolling Bearing ◽  
Gaussian Mixture ◽  
Remaining Useful Life ◽  
Joint Probability Distribution ◽  
Rolling Bearings ◽  
Two Stage ◽  
Stochastic Filtering ◽  
Useful Life

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

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