Authors:
Tarak Benkedjouh
1
;
Noureddine Zerhouni
2
and
Said Rechak
3
Affiliations:
1
EMP and Laboratoire Mécaniques des Structures, Algeria
;
2
FEMTO-ST Institute UMR CNRS 6174, France
;
3
ENP and Laboratoire Génie Mécanique, Algeria
Keyword(s):
Blind Sources Separation, Empirical Modes Decomposition, Robust Correlation, Prognostics, bearings, RUL.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Computer-Based Manufacturing Technologies
;
Engineering Applications
;
Environmental Monitoring and Control
;
Image and Video Analysis
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Manufacturing Systems Engineering
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time-Frequency Analysis
Abstract:
Prognostics and Health Management (PHM) for condition monitoring systems have been proposed for predicting
faults and estimating the remaining useful life (RUL) of components or subsystem. For gaining importance
in industry and decrease possible loss of production due to machine stopping, a new intelligent method
for bearing health assessment based on Empirical mode decomposition (EMD) and Blind Source Separation
(BSS). EMD is one of the most powerful time-frequency analysis decompose the signal into a set of orthogonal
components called intrinsic mode functions (IMFs). BSS method used to separate IMFs of one-dimensional
time series into independent time series. The health indicator based on the robust correlation coefcient is
proposed based on a weighted average correlation calculated from different combinations of the original data.
The correlation coefficients between separated IMFs used to estimate the health of bearing; The correlation
coefficient used for comparison be
tween the estimated sources with differents degradation levels. The correlation
coefficient values are then fitted to a regression to obtain the model for Remaining Useful Life (RUL)
estimation. The method is applied on accelerated degradations bearings called PRONOSTIA. Experimental
results show that the proposed method can reflect effectively the performance degradation of bearing.
(More)