Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Emmanuel Ramasso and Abhinav Saxena
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_14_063.pdf195.73 KBSeptember 12, 2014 - 3:59am

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
063
Page Count: 
11
Submission Keywords: 
prognostics
Remaining Useful Life Estimation
CMAPSS dataset
Review
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Uncertainty Quantification and Management in PHM
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