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An attempt at applying machine learning in diagnosing marine ship engine turbochargers
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Online publication date: 2022-11-09
Publication date: 2022-11-09
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(4):795–804
  • Machine learning simplified the decision to renew the turbocharging system of the marine engine.
  • The set of controlled parameters was minimized for the needs of diagnostic relationships.
  • Ease of making maintenance decisions based on the maintenance requirement index.
  • The results were verified with experimental data from engine tests of two types of turbochargers
The article presents a diagnosis of turbochargers in the supercharging systems of marine engines in terms of maintenance decisions. The efficiency of turbocharger rotating machines was defined. The operating parameters of turbocharging systems used to monitor the correct operation and diagnose turbochargers were identified. A parametric diagnostic test was performed. Relationships between parameters for use in machine learning were selected. Their credibility was confirmed by the results of the parametric test of the turbocharger system and the main engine, verified by the coefficient of determination. A particularly good fit of the describing functions was confirmed. As determinants of the technical condition of a turbocharger, the relationship between the rotational speed of the engine shaft, the turbocharger rotor assembly and the charging air pressure was assumed. In the process of machine learning, relationships were created between the rotational speed of the engine shaft and the boost pressure, and the indicator of the need for maintenance. The accuracy of the maintenance decisions was confirmed by trends in changes in the efficiency of compressors
Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters
Jan Monieta, Lech Kasyk