Towards a Hybrid Framework For Prognostics With Limited Run-to-Failure Data

The introduction of cyber-physical systems with increased
availability of sensor data creates a lot of research interest in
prognostic algorithms for predictive maintenance. Although a
lot of algorithms are successfully applied to benchmark case
studies based on simulated data and experimental set-ups, deployment
in industry lags behind. From a comparison between
three benchmark case studies with two real-world case studies
based on prognostic metrics (monotonicity, prognosability
and trendability), two main issues are observed: 1) the lack
of run-to-failures and 2) low prognostic metrics due to a low
signal-to-noise ratio of degradation trends, as a result of unexplained
physical phenomena. To make prognostics feasible,
a hybrid framework is proposed that focuses on improving
system knowledge. The framework consists of a quantitative
diagnostic assessments, guided by (modular) system models
in which damage is induced. This quantitative damage assessment
provides input for prognostics based on Bayesian
filtering, enabling prognostics for assets in varying operational
conditions. Implementation and validation of the framework
requires investments, but modularity within the framework
can accelerate development for new systems.