NON-PEER REVIEW
20
th
Australian International Aerospace Congress, 27-28 November 2023, Melbourne
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Using accurate online oil condition monitoring sensor data
to improve HUMS
Greg Horwich
1
, Dr. Timothy Mack
1
1
Gastops Ltd, 1011 Polytek Street, Gloucester, ON, K1J 9J3, Canada
Abstract
High value equipment including aviation turbine engines and medium and large diesel
engines require Health and Usage Monitoring Systems (HUMS) which provide actionable and
reliable output. Many component failures in oil lubricated systems can be attributed directly
to oil condition and contamination, yet the measurement of lubricating oil health using online
sensors does not currently provide sufficiently accurate data to facilitate maintenance
decisions. In this paper, the direct measurement of oil condition and contamination is
discussed through the use of new sensing technologies which give a direct measurement of oil
antioxidants and oxidation by-products, as well as quantified measures of water, fuel, coolant,
and soot and carbon contamination. By generating unambiguous data and directly measuring
individual parameters of the lubricating oil, advanced data processing including physics-based
digital twin modelling and machine learning techniques can be applied to the generated data
to accurately diagnose lubricant health and identify issues that may affect equipment health.
The enhanced model can be used to accurately diagnose lubricant health in near-real-time.
This paper presents a novel sensing technology for measuring oil condition accurately in an
online, real-time environment, facilitating future work in the development of improved
HUMS to facilitate a shift of the most common predictive maintenance tasks to condition-
based maintenance tasks, and ultimately to true predictive maintenance.
Keywords: Oil condition monitoring, online sensor, aviation, diesel engine, condition-based
maintenance
Introduction
High value equipment including aviation turbine engines and medium and large diesel
engines require Health and Usage Monitoring Systems (HUMS) which provide actionable and
reliable output. Many component failures in oil lubricated systems can be attributed directly
to oil condition and contamination, yet the measurement of lubricating oil health using online
sensors does not currently provide sufficiently accurate data to facilitate maintenance
decisions. Currently available technologies may provide ambiguous condition indicators (CI)
and unreliable data with respect to equipment health status. CIs are the parameters used in
condition-based maintenance activity to measure the “health” of machinery and to establish
the point at which maintenance must be triggered. The CI must identify the impending failure
in an unambiguous, statistically reliable, and accurate fashion, while providing sufficient
advanced notice to permit maintenance to be scheduled. A condition indicator can be derived
from one or several parameters which can be measured during operation. Common
