RULCLIPPER algorithm and CMAPSS health indicators
Implements an algorithm for "Remaining Useful Life estimation based on impreCise heaLth Indicator modeled by Planar Polygons and similarity-basEd Reasoning" (RULCLIPPER) initially developed for prognostics on CMAPSS datasets. It makes use of elementary polygon operations implemented on Matlab together with recall/precision/f1-measure for similarity estimation. In contrast to its simplicity, it provided very good results on CMAPSS datasets (publication included) compared to many other approaches including neural network or Bayesian (sparse) learning. The package includes a way to estimate health indicators on those datasets (proposed in the publication). Running codes allow to retrieve results of the publication.
Cite As
Emmanuel Ramasso (2024). RULCLIPPER algorithm and CMAPSS health indicators (https://www.mathworks.com/matlabcentral/fileexchange/54866-rulclipper-algorithm-and-cmapss-health-indicators), MATLAB Central File Exchange. Retrieved .
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RULCLIPPER - Prognostics/Codes/
Version | Published | Release Notes | |
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1.0.0.0 | just some additional comments in codes |