Diagnostic fault detection for internal combustion engines via pressure curve reconstruction

Brian J. Murphy, Mitchell S. Lebold, Karl Reichard, T. Galie, C. Byington

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


One proven technique for monitoring the health of a sealed internal combustion engine is to analyze the combustion pressure cycle curves of the individual cylinders. Current techniques for doing this require a pressure sensor mounted directly in the combustion chamber. This necessitates maintenance and design considerations that may be unacceptable especially on legacy systems. This paper describes a non-invasive technique developed for monitoring combustion pressure cycle related faults. This method has been developed and tested on a diesel engine test bed at Penn State University's Applied Research Laboratoq (ARL), Condition Based Maintenance Department. The diesel engine test bed was used to gather all forms of data under different engine operating conditions. Using crdshaft angular velocity data fiom a high-resolution encoder, a trained neural network is used to reconstruct the combustion pressure cycle curves. These reconstructed combustion pressure curves are then passed into another trained neural network for fault detection analysis.

Original languageEnglish (US)
Title of host publication2003 IEEE Aerospace Conference, Proceedings
Number of pages8
StatePublished - 2003
Event2003 IEEE Aerospace Conference - Big Sky, MT, United States
Duration: Mar 8 2003Mar 15 2003

Publication series

NameIEEE Aerospace Conference Proceedings
ISSN (Print)1095-323X


Other2003 IEEE Aerospace Conference
Country/TerritoryUnited States
CityBig Sky, MT

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science


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