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Modeling and Excitation Torque Estimation of an Engine Cold-Test Stand for Evaluating Driveline Design and Fault Diagnostics

[+] Author Affiliations
Kamran A. Gul, Douglas E. Adams

Purdue University, West Lafayette, IN

Paper No. IMECE2007-43255, pp. 697-706; 10 pages
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 10: Mechanics of Solids and Structures, Parts A and B
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4304-1 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME


The fault detection process becomes difficult in engine cold-testing when the test stands undergo torsional vibrations excited by various engine harmonics. In the context of vibration suppression and assembly faults diagnostics, this paper focuses on three main aspects: cold-test stand modeling and validation, estimation of cold-engine excitation torque, and evaluation of a fuel injector pulse diagnostic test. To analyze the vibratory torque of the cold-test stand driveline, an engine excitation model comprising of inertia torque and unfired gas pressure torque is needed. The gas pressure torque cannot be determined in the absence of engine gas pressure measurements. It is shown that this excitation torque can be estimated by assuming that the net torque during the unfired engine cycle is zero and by utilizing the firing engine gas pressure torque in the combustion stroke. The estimated excitation torque is employed to validate models of different cold-test stands and also to examine various driveline design alternatives. Using embedded sensitivity functions, it is found that by changing the inertia and stiffness of the rubber coupling, the torsional vibrations can be suppressed. Finally, the modified design is evaluated for an injector pulse diagnostic test. It is shown that the design modifications can also enhance sensitivity to faults if the injector pulse test is carried out at higher speeds.

Copyright © 2007 by ASME
Topics: Torque , Engines , Design , Modeling



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