Method for predicting the engine oil condition of ultralight aircraft on the basis of its operational history

Funding from the Ministry of Education and Science

Project title: Method for predicting the engine oil condition of ultralight aircraft on the basis of its operational history

Agreement number: DWD/6/0117/2022

Project implementation period: 01.10.2022 - 30.09.2026

Principal Investigator: dr hab. inż. Jacek Czarnigowski, prof. uczelni

Project value: 395 442,55 PLN

Funds granted for Lublin University of Technology: 395 442,55 PLN

Abstract: The aim of the project, which is being carried out in the discipline of mechanical engineering, is to develop a method for predicting the oil degradation status of the engine of an ultralight aircraft propulsion system based on its service history.

Currently, aircraft operation is based on the resourse method - fixed time steps. It does not take into account differences in the use of the aircraft and therefore differences in the degree of wear. This leads, on the one hand, to the possibility of overlooking the harbingers of failure and, on the other hand, to the replacement of components that are still within acceptable service parameters. This is particularly important in the case of engine oil.

The aim of the project is to develop an maintenance method based on condition analysis. The method will be based on correlating the selected aircraft operating parameters, recorded during normal operation, with the condition of the oil and, on this basis, predicting when to replace it. The project will cover the scope of the discipline of Mechanical Engineering, in particular the scope of construction and maintenance of machine (aircraft). It is planned to carry out a series of supervised operation experiments on a selected group of aircraft (a minimum of 3 rotorcraft), during which its operation parameters and periodic oil samples will be collected. A regression analysis of selected oil parameters (related to oil consumption) with the aircraft's history of use in the form of its operating parameters will be carried out. On this basis, a predictive model of oil consumption status will be made, which is the main element of the implementation.