Investigating computational geometry for failure prognostics in presence of imprecise health indicator: Results and comparisons on C-MAPSS datasets

Emmanuel Ramasso
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Full Paper
phmce_14_002.pdf543.31 KBMay 29, 2014 - 4:03am

Prognostics and Health Management (PHM) is a multidisciplinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system's health state. A health index (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted by developing a similarity measure to estimate the RUL. This adaptation makes use of computational geometry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIPPER is assessed and compared on datasets generated by the NASA's turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM'08 data challenge. These datasets represent 1360 testing instances and cover different realistic and difficult cases considering operating conditions and fault modes with unknown characteristics. The problem of feature selection, health index estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few parameter tuning on all datasets.

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Submission Keywords: 
Pattern matching
Instance-based learning
Prediction method
Data Uncertainty
CMAPSS datasets
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Uncertainty Quantification and Management in PHM
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