Utilizing Tribological System Parameters as a Harbinger of Distress in Dynamically and Aero-Thermally Coupled Systems

[+] Author and Article Information
Sheridon Haye

106 Highland Rd Mansfield Center, CT 06250 Sheridon.Haye@pw.utc.com

1Corresponding author.

Manuscript received November 29, 2018; final manuscript received April 9, 2019; published online xx xx, xxxx. Assoc. Editor: Henrique Reis.

ASME doi:10.1115/1.4043503 History: Received November 29, 2018; Accepted April 09, 2019


This paper summarizes a data fusion approach for utilizing conventional lubrication parameters in an unconventional method for identifying deterioration in a thermally coupled system. Complex machines are composed of multiple systems that are intrinsically dependent. Design of these systems requires expertise in distinct disciplines with a determined focus on meeting system specific requirements. This expertise focused approach promotes a silo mindset to system design, which is then carried through to the design and implementation of the health management system of these machines. These multi-disciplinary interacting systems are traditionally monitored as independent entities, with little advantage taken, of the direct and cross coupled effects. For example, parameters required for lubrication health monitoring include, but are not limited to, oil pressure and temperature. How these parameters change can be indicative of the health of interacting systems otherwise considered independent and isolated. By exploring the rationale of the cross system impacts, physical interactions between these systems (albeit empirical) can be used for cross system monitoring. To achieve this objective parameters that are measured in one system are used to determine the diagnostic state of another coupled system with limited, or no, system visibility. A fuzzy logic fusion approach is employed in this task and was designed and implemented for the above mentioned purpose. The focus of interest was on the lubrication and hot section interactions with parameters obtained from real machines. Fuzzy membership functions and rules were determined and tuned appropriately from real data and applied to nominal and defective machines.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In