Remaining useful life prediction model based on gradient feature stochastic filtering

2012 ◽  
Author(s):  
Sun Lei ◽  
Wang Wei-guo ◽  
Lin Guo-yu
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Stochastic Filtering ◽  
Model Based ◽  
Useful Life ◽  
Gradient Feature ◽  
Life Prediction Model

Remaining Useful Life Prediction Model Based on Gradient Feature Stochastic Filtering

2013 ◽  
Vol 380-384 ◽  
pp. 1151-1155
Author(s):  
Lei Sun ◽  
Gang Li ◽  
Gang Li
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Engine Oil ◽  
Model Parameters ◽  
Filter Model ◽  
Stochastic Filtering ◽  
Useful Life ◽  
The Impact ◽  
Life Prediction Model

This paper proposed a remaining useful life prediction model to avoid the original monitoring information due to the influence of the oil monitoring linear regression process, thereby reducing the prediction error. According to the process of equipment wear, we analyzed the impact of the relationship between the wear, the metal particle concentration and the remaining useful life; then established an improved filter model. Using maximum likelihood parameter to estimate model parameters. Finally, taking a certain type of self-propelled Gun Engine Oil Spectrum Data for example, and the results show that the remaining useful life prediction model of equipment has a certain practical value.

scholarly journals Remaining useful life prediction model of the space station

2019 ◽  
Vol 21 (3) ◽  
pp. 501-510
Author(s):  
Xiaopeng Li ◽  
Hong-Zhong Huang ◽  
Fuqiu Li ◽  
Liming Ren
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Space Station ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Life Prediction Model

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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