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An attempt at applying machine learning in diagnosing marine ship engine turbochargers

Adamkiewicz Andrzej, Nikończuk Piotr

Eksploatacja i Niezawodność

2022

Vol. 24, no. 4

795--804

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.

Passive operating system fingerprinting using neural networks and induction of decision rules

Bielski B., Klęsk P.

Metody Informatyki Stosowanej

2008

nr 4 (Tom 17)

15--25

One of the most difficult task for people managing big- or even medium-size computer network is determining the accurate number of hosts that are protected. This information is really helpful for accurately configuring network-based devices such as intrusion detection systems. Exact knowledge of the operating systems (residing in hosts) can be useful for excluding many alerts that cannot apply to a remote operating system that is being examined. In this context, we consider a classification problem (we try to recognize the class of operating system) when some of the characteristics of the system are modified by its user or any other program (e.g. for internet connection tuning). We use neural networks (MLP, RBF) and rule induction techniques. It should be stressed that existing fingerprinting tools get high accuracy results when tested on the “clean” versions of operating systems, but they fail to detect systems with modified TCP/IP parameters.