Abstract
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 language | English (US) |
|---|---|
| Title of host publication | 2003 IEEE Aerospace Conference, Proceedings |
| Pages | 3239-3246 |
| Number of pages | 8 |
| Volume | 7 |
| DOIs | |
| State | Published - Dec 1 2003 |
| Event | 2003 IEEE Aerospace Conference - Big Sky, MT, United States Duration: Mar 8 2003 → Mar 15 2003 |
Other
| Other | 2003 IEEE Aerospace Conference |
|---|---|
| Country | United States |
| City | Big Sky, MT |
| Period | 3/8/03 → 3/15/03 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Space and Planetary Science
Cite this
}
Diagnostic fault detection for internal combustion engines via pressure curve reconstruction. / Murphy, Brian J.; Lebold, Mitchell S.; Reichard, Karl Martin; Galie, T.; Byington, C.
2003 IEEE Aerospace Conference, Proceedings. Vol. 7 2003. p. 3239-3246 1234167.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Diagnostic fault detection for internal combustion engines via pressure curve reconstruction
AU - Murphy, Brian J.
AU - Lebold, Mitchell S.
AU - Reichard, Karl Martin
AU - Galie, T.
AU - Byington, C.
PY - 2003/12/1
Y1 - 2003/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84877490148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877490148&partnerID=8YFLogxK
U2 - 10.1109/AERO.2003.1234167
DO - 10.1109/AERO.2003.1234167
M3 - Conference contribution
AN - SCOPUS:84877490148
SN - 078037651X
SN - 9780780376519
VL - 7
SP - 3239
EP - 3246
BT - 2003 IEEE Aerospace Conference, Proceedings
ER -