Bearings Prognostics based on Blind Sources Separation and Robust Correlation Analysis

Tarak Benkedjouh, Noureddine Zerhouni, Said Rechak

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 between 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.

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Paper Citation


in Harvard Style

Benkedjouh T., Zerhouni N. and Rechak S. (2017). Bearings Prognostics based on Blind Sources Separation and Robust Correlation Analysis . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 658-663. DOI: 10.5220/0006472006580663


in Bibtex Style

@conference{icinco17,
author={Tarak Benkedjouh and Noureddine Zerhouni and Said Rechak},
title={Bearings Prognostics based on Blind Sources Separation and Robust Correlation Analysis},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={658-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006472006580663},
isbn={978-989-758-263-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Bearings Prognostics based on Blind Sources Separation and Robust Correlation Analysis
SN - 978-989-758-263-9
AU - Benkedjouh T.
AU - Zerhouni N.
AU - Rechak S.
PY - 2017
SP - 658
EP - 663
DO - 10.5220/0006472006580663