A decision-tree-based approach to smoke spike detection in a heavy-duty diesel engine

Smoke spikes occurring during transient engine operation have detrimental health effects and increase fuel consumption by requiring more frequent regeneration of the diesel particulate filter. This paper proposes a decision tree approach to real-time detection of smoke spikes for control and on-board diagnostics purposes. A contemporary, electronically controlled heavy-duty diesel engine was used to investigate the deficiencies of smoke control based on the fuel-to-oxygen-ratio limit. With the aid of transient and steady state data analysis and empirical as well as dimensional modeling, it was shown that the fuel-to-oxygen ratio was not estimated correctly during the turbocharger lag period. This inaccuracy was attributed to the large manifold pressure ratios and low exhaust gas recirculation flows recorded during the turbocharger lag period, which meant that engine control module correlations for the exhaust gas recirculation flow and the volumetric efficiency had to be extrapolated. The engine control module correlations were based on steady state data and it was shown that, unless the turbocharger efficiency is artificially reduced, the large manifold pressure ratios observed during the turbocharger lag period cannot be achieved at steady state. Additionally, the cylinder-to-cylinder variation during this period were shown to be sufficiently significant to make the average fuel-to-oxygen ratio a poor predictor of the transient smoke emissions. The steady state data also showed higher smoke emissions with higher exhaust gas recirculation fractions at constant fuel-to-oxygen-ratio levels. This suggests that, even if the fuel-to-oxygen ratios were to be estimated accurately for each cylinder, they would still be ineffective as smoke limiters. A decision tree trained on snap throttle data and pruned with engineering knowledge was able to use the inaccurate engine control module estimates of the fuel-to-oxygen ratio together with information on the engine control module estimate of the exhaust gas recirculation fraction, the engine speed, and the manifold pressure ratio to predict 94% of all spikes occurring over the Federal Test Procedure cycle. The advantages of this non-parametric approach over other commonly used parametric empirical methods such as regression were described. An application of accurate smoke spike detection in which the injection pressure is increased at points with a high opacity to reduce the cumulative particulate matter emissions substantially with a minimum increase in the cumulative nitrogen oxide emissions was illustrated with dimensional and empirical modeling.

• Record URL:
  https://doi.org/10.1177/0954407012464314
• Record URL:
  http://pid.sagepub.com/cgi/content/abstract/227/8/1112
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • Reprinted by permission of Sage Publications, Ltd.
• Authors:
  • Brahma, Indranil
• Publication Date: 2013-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References;
• Pagination: pp 1112-1129
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 227
  • Issue Number: 8
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Data mining; Decision trees; Diesel engine exhaust gases; Diesel engines; Fuel air mixtures; Heavy duty vehicles; Particulates
• Subject Areas: Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01489818
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 15 2013 9:13AM