Feature Extraction and Evaluation for Health Assessment and Failure Prognostics

K. Medjaher, F. Camci, and N. Zerhouni
Submission Type: 
Full Paper
phmce_12_028.pdf226.84 KBMay 30, 2012 - 4:23am

The estimation of Remaining Useful Life (RUL) of industrial equipments can be realized on their most critical components. Based on this assumption, the identified critical component must be monitored to track its health state during its operation. Then, the acquired data are processed to extract relevant features, which are used for RUL estimation.
This paper presents an evaluation method for the goodness of the features, extracted from raw monitoring signals, for health assessment and prognostics of critical industrial components. The evaluation method is applied to several simulated datasets as well as features obtained from a particular application on bearings. The obtained simulation results are presented and discussed at the end of the paper.

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Submission Keywords: 
condition monitoring
fault detection
fault diagnostics
feature extraction
Remaining useful Life
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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