Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty

Abstract

Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics tools
in order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operational
uncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impacts
of age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be captured
through a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisition
pipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertainties
can disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it has
progressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generally
undermines model confidence.

One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledge
or data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations to
offset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historical
data to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensures
the model is updated and representative of the real monitored asset by accounting for differences between the physics or
knowledge model and CM data.

In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilized
in an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in the
development of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impact
on RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designs
which generated different predicted RUL trajectories, increasing the uncertainty in the model output.