Six years and more than seventy publications later this paper looks back and analyzes the development of prognostic algo-rithms using C-MAPSS datasets generated and disseminated by the prognostic center of excellence at NASA Ames Re-search Center. Among those datasets are five run-to-failure C-MAPSS datasets that have been popular due to various char-acteristics applicable to prognostics. The C-MAPSS datasets pose several challenges that are inherent to general prognos-tics applications. In particular, management of high vari-ability 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 capabil-ities of prognostics algorithms. More than seventy publica-tions have used the C-MAPSS datasets for developing data-driven prognostic algorithms. However, in the absence of per-formance benchmarking results and due to common misun-derstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably compare their results. In addition to identifying differentiating char-acteristics in these datasets, this paper also provides perfor-mance results for the PHM’08 data challenge wining entries to serve as performance baseline. This paper summarizes var-ious prognostic modeling efforts that used 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. 1.

Benchmarking of prognostic algorithms has been challeng-ing due to limited availability of common datasets suit-able for prognostics. In an attempt to alleviate this prob-lem, several benchmarking datasets have been collected by NASA’s prognostic center of excellence and made available to the Prognostics and Health Management (PHM) commu-nity to allow evaluation and comparison of prognostics algo-rithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteris-tics 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, man-agement 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 de-veloping 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 sev-eral key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions as-sessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summa-rizes 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. 1.