Degradation prognosis based on a model of Gamma process mixture

Edith Grall-Maës, Pierre Beauseroy, and Antoine Grall
Submission Type: 
Full Paper
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phmce_14_036.pdf271.42 KBMay 26, 2014 - 1:30am

A novel method is proposed to exploit jointly degradation measurements originating from a set of identical systems for making a degradation prognosis. The systems experience different degradation processes depending on operational conditions. The degradation processes are assumed to be Gamma processes. The aim is to cluster the degradation paths in classes corresponding to the different operational conditions in order to group properly the data for the estimation of degradation process parameters. A model of Gamma process mixture is considered and an expectation-minimization approach is proposed to estimate the unknown parameters. The feasibility of the method is shown on simulated cases. Prognosis results obtained with the proposed method are compared with results obtained with basic strategies (considering each system alone or all system together).

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
036
Page Count: 
8
Submission Keywords: 
gamma process
clustering
data-driven prognosis
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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