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Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service.

Tomas Harach1, Petr Simonik1, Adela Vrtkova2

  • 1Department of Electronics, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15/2172, 708 00 Ostrava-Poruba, Czech Republic.

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Summary

A new machine learning powertrain diagnostics platform analyzes vehicle emissions and operational data to detect engine defects. This system identifies issues missed by standard onboard diagnostics, even in advanced vehicles.

Keywords:
PaaScloud computingexhaust emission testing and evaluationnew emission measurement methodsquantile regression

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Area of Science:

  • Automotive Engineering
  • Machine Learning Applications
  • Environmental Science

Background:

  • Modern vehicles require advanced diagnostics beyond standard onboard systems.
  • Emission data from inspection stations offers a valuable resource for powertrain health assessment.
  • Existing diagnostic methods struggle to detect certain internal combustion engine defects.

Purpose of the Study:

  • To introduce a novel powertrain diagnostics platform for EU inspection stations.
  • To develop a machine learning technique for identifying specific engine dysfunctions.
  • To demonstrate the platform's capability in detecting defects missed by current vehicle self-diagnostics.

Main Methods:

  • Utilized emission measurement data and vehicle operational parameters from numerous EU inspection stations.
  • Applied a machine learning model trained on 9 static testing points, combustion chamber volume, EURO emission standard, engine condition, and mileage.
  • Employed quantile regression for fleet-level evaluation of diagnostic accuracy.
  • Collected and processed data using the Mindsphere cloud platform as a core PaaS solution.

Main Results:

  • Successfully developed and implemented a powertrain diagnostics platform.
  • Demonstrated the capability to classify combustion engine dysfunctions using the developed method.
  • Identified specific engine defects not detectable by self-diagnostic systems up to EURO 6 standards.
  • Validated the platform's effectiveness using real-world data and simulated defect scenarios.

Conclusions:

  • The new platform offers a significant advancement in powertrain diagnostics for vehicle inspection.
  • Machine learning applied to emission and operational data can effectively detect subtle engine defects.
  • This approach enhances vehicle safety and environmental compliance by identifying previously undetectable issues.