Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and K-means clustering

2020 ◽  
Vol 31 (8) ◽  
pp. 085601 ◽  
Author(s):  
Jaskaran Singh ◽  
A K Darpe ◽  
S P Singh
Keyword(s):  
Change Point ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Health State ◽  
State Change ◽  
Life Estimation ◽  
Model Based ◽  
Useful Life

Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

2019 ◽  
Vol 184 ◽  
pp. 228-239 ◽  
Author(s):  
Marcia Baptista ◽  
Elsa M.P. Henriques ◽  
Ivo P. de Medeiros ◽  
Joao P. Malere ◽  
Cairo L. Nascimento ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Life Estimation ◽  
Combining Data ◽  
Useful Life

scholarly journals A data-driven framework for remaining useful life estimation

2017 ◽  
Vol 55 (5) ◽  
pp. 557 ◽  
Author(s):  
Hoa Dinh Nguyen
Keyword(s):  
Data Processing ◽  
Health Management ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Life Estimation ◽  
Search Experiment ◽  
Useful Life ◽  
Structural Health Management ◽  
Processing Techniques ◽  
Data Library

Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete  run of the system, the RUL can be predicted  using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and  in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches  are effective in performing RUL prediction.

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