Online Real-Time Tribology Failure Detection System

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$100,000.00
Award Year:
2006
Program:
STTR
Phase:
Phase I
Contract:
NND06AA37C
Agency Tracking Number:
050141
Solicitation Year:
2005
Solicitation Topic Code:
T2.02
Solicitation Number:
n/a
Small Business Information
Migma Systems, Inc.
1600 Providence Highway, Walpole, MA, 02081-2553
Hubzone Owned:
N
Socially and Economically Disadvantaged:
Y
Woman Owned:
Y
Duns:
125933916
Principal Investigator:
Bo Ling
Principal Investigator
(508) 660-0328
bling@migmasys.com
Business Contact:
Bo Ling
Business Official
(508) 660-0328
bling@migmasys.com
Research Institution:
Louisiana State University
Not L Available
Department of Mechanical Engineering
Baton Rouge, LA, 70803
(225) 578-9192
Domestic nonprofit research organization
Abstract
The investigation of the coating friction as a function of time is important to monitor the ball bearing heath. Despite the importance of the subject mater, there is a crucial lack of information in the literature about coating life and friction force in ball bearings as coating wear of progressively increases. Here we propose to develop a strategic space vehicle health monitoring system that will identify potential and/or imminent lubrication problems, analyze these parameters in real time, and provide direct input so that these problems are mitigated prior to failure. We will set up a lab experiment environment with a universal microtribometer and acoustic emission sensors measuring the signals associated with wear and the changes that tend to occur as a function of time. Friction force and acoustic signal will be measured with respect to the bearing condition. To capture the dynamic nature of friction evolution, we propose to extract the temporal transient features from the sensing data and develop Hidden Markov Models with four distinct states associated with four operation conditions of the ball bearing. Our system uniquely combine both physics-based and stochastic models for the online diagnosis.

* information listed above is at the time of submission.

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