Lubricant Condition Monitoring

Grant Number: IIP No. 1940879

Name: Translating an intelligent lubricant condition monitoring system into a commercially viable prototype

Start Date: March 9, 2020

Investigator(s): Jiang Zhe (Principal Investigator)

Gopal Nadkarni (Co-Principal Investigator)

NSF Site

Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to develop an intelligent online real-time lubricant oil health monitoring system to maintain and extend the health of high-speed rotating and reciprocating machinery used in many key industries. The completed system will significantly reduce operation costs by eliminating the need for inspection shutdowns, while avoiding catastrophic component failure during operation. Applications include bearings, turbomachinery gearboxes, and even combustion engines in sectors such as the wind farm, transportation, manufacturing and defense industries. The system is expected to provide an important early warning of mechanical failure and will be used to develop more efficient maintenance schedules to reduce machine operation costs and avoid catastrophic failure.

The proposed project aims to develop a comprehensive lubricant oil condition monitor system offering real-time characterization of contaminant debris and lubricant oil quality to judge a machine?s operating health. Objectives include: 1) inductive pulse sensing and microfluidic signal multiplexing technologies will be used to detect fine wear debris in lubricant oil to enable monitoring of the entire wear progression; 2) real-time processing will be used to significantly reduce the data size to be analyzed and stored; 3) a lubricant property-sensing array will measure multiple oil properties simultaneously; 4) an artificial neural network will link measured sensor signals to oil properties with high accuracy, eliminating cross-sensitivities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.