Research Papers

Utilizing Tribological System Parameters as a Harbinger of Distress in Dynamically and Aerothermally Coupled Systems

[+] Author and Article Information
Sheridon Haye

Pratt & Whitney,
Department of Control and Diagnostics,
400 Main Street,
East Hartford, CT 06118
e-mail: sheridon.haye@pw.utc.com

Manuscript received November 29, 2018; final manuscript received April 9, 2019; published online May 17, 2019. Assoc. Editor: Henrique Reis.

ASME J Nondestructive Evaluation 2(2), 021004 (May 17, 2019) (10 pages) Paper No: NDE-18-1043; 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 multidisciplinary 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. These parameters are critical in determining the health of the lubrication system. However, how these parameters change can be an 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 knowledge) can be used for cross-system monitoring. A means of achieving this objective is to utilize parameters that are measured in one system to determine the diagnostic state of another coupled system with limited, or no, system observability. 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.


Kyriacos, U., Jelsma, J., and Jordan, S., 2011, “Monitoring Vital Signs Using Early Warning Scoring Systems: A Review of the Literature,” J. Nurs. Manag., 19(3), pp. 311–330. [CrossRef] [PubMed]
Dvorák, A., and Novák, V., 2004, “Fuzzy Logic as a Methodology for the Treatment of Vagueness,” The Logica Yearbook, L. Behounek, and M. Bilkova, eds., Filosofia, Prague, pp. 141–151.
Rajora, R., and Dixit, H. K., 2013, “Effect of Lube Oil Temperature on Turbine Shaft Vibration,” J. Mech. Eng. Robot. Res., 2(2), pp. 324–334.
Volponi, A., Brotherton, T., and Luppold, R., 2004, “Development of an Information Fusion System for Engine Diagnostics and Health Management,” AIAA 1st Intelligent Systems Technical Conference, Chicago, IL, Sept. 20–22.
Zadeh, L. A., 1997, “Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic,” Fuzzy Sets Syst., 90(2), pp. 111–127. [CrossRef]
Dempsey, P. J., and Afjeh, A. A., 2004, “Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic,” J. Am. Helicopter. Soc., 49(2), pp. 109–116. [CrossRef]
Byington, C. S., Merdes, T. A., and Kozlowski,, J. D., 1999, “Fusion Techniques for Vibration and Oil Debris/Quality in Gearbox Failure Testing,” Proceedings of International Condition Monitoring Conference, University of Wales, Swansea, Apr. 12–16, M. H. Jones and D. G. Sleeman, eds., Coxmoor Publishing, Oxford.


Grahic Jump Location
Fig. 1

Normalized pressure versus normalized power showing scatter

Grahic Jump Location
Fig. 2

Measured, predicted, and delta measure pressure over normalized machine power setting

Grahic Jump Location
Fig. 3

Effect of displaying pressure trend against (a) speed and (b) time

Grahic Jump Location
Fig. 4

Wide scatter of temperature for test articles

Grahic Jump Location
Fig. 5

Measured, predicted, and derived delta measure of temperature over normalized power

Grahic Jump Location
Fig. 6

Measured, predicted, and derived delta measure of temperature versus time

Grahic Jump Location
Fig. 7

Fuzzy fusion structure with parameters of interest applied

Grahic Jump Location
Fig. 8

Membership function fuzzification

Grahic Jump Location
Fig. 10

Fusion results for the eight machines show clear indication of failure that align with hardware observations



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