Data Selection Criteria for the Application of Predictive Maintenance to Centrifugal Pumps

Abstract

The maintenance of vehicles and components is present in
most people’s daily lives, ranging from changing a private
vehicle’s oil to the failure prediction of an aircraft component
during flight. Usually, the manufacturer’s maintenance
recommendation is a good solution when the cost is not too
high, and the real application is used as indicated by the manufacturer.
However, this recommendation can turn unfeasible
when there is a significant variation in operational conditions
or high maintenance costs. In these cases, the manufacturer’s
suggestion is typically conservative, leading to unnecessarily
high costs. Therefore, the challenge is to find the best approach
for optimizing a component’s maintenance, given the
system in which it is integrated and the associated operational
and environmental conditions. Nevertheless, the available information
on the loads on the component also plays a role in
that choice. This paper proposes to combine case-specific information
with generic degradation prediction models to obtain
an acceptable but also affordable approach. The objective
is to develop data selection criteria to indicate the parameters
that have a high impact on the failure prediction, in this case,
of a generic impeller pump. Subsequently, the approach delivers
to the user an indication of the component remaining
useful life using different operational scenarios.