PFsuper: Simulation-Based Prognostics to Monitor and Predict Sparse Time Series

Javier Echauz, Andrew Gardner, Ryan R. Curtin, Nikolaos Vasiloglou, and George J. Vachtsevanos
Submission Type: 
Full Paper
phmc_17_061.pdf2.8 MBSeptember 6, 2017 - 3:57pm

Commercial systems for predicting remaining useful life (RUL) of serviceable parts like engine oil tend to use either generic regression models (practical, e.g., widely deployed in the automotive industry), or dynamic models for which software lags behind theory (impractical, e.g., ‘one-trick’ hardcodings). We describe an arguably more realistic framework using both generic and vehicle-specific dynamic models of time-series for simulation-based condition monitoring and RUL forecasting, suitable in situations where: (a) measured time-series are sparse or slowly sampled, and (b) health condition signals tend to follow relatively simple paths (low-degree polynomial stationary trends, unit-root stochastic trends, exponential growths, quasiperiodic oscillations). This combination unlocks affordability of PFsuper, a prognostics algorithm that implements online Bayesian learning with particle filters to jointly estimate hidden condition state and optionally a handful of unknown parameters, coupled with subsimulations characterizing failure progression and RUL probability density function. The overall method converts a generic static time-as-a-regressor model into a stochastic differential equation, then has PFsuper adapt the initially generic model into a vehicle-specific one as data measurements arrive.

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Submission Keywords: 
Model-based Prognostics; Parameter Estimation; Particle Filtering; Simulation; Oil Residual Useful Life
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Uncertainty Quantification and Management in PHM
Verification and validation
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