A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

Aleksandra Grzesiek, Radosław Zimroz, Paweł Śliwiński, Norbert Gomolla and Agnieszka Wyłomańska
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Aleksandra Grzesiek: Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Radosław Zimroz: Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Paweł Śliwiński: KGHM Polska Miedź S.A., M. Skłodowskiej-Curie 48, 59-301 Lubin, Poland
Norbert Gomolla: DMT GmbH & Co. KG, Am Technologiepark 1, 45307 Essen, Germany
Agnieszka Wyłomańska: Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology, Wyspiańskiego 27, 50-370 Wrocław, Poland

Energies, 2021, vol. 14, issue 17, 1-24

Abstract: In this paper, a heavy-duty loader operated in an underground mine is discussed. Due to extremely harsh operational conditions, an important maintenance problem is related to engine oil pressure. We have found that when the degradation process appears, the nature of variation of pressure engine oil changes. Following this observation, we have proposed a data analysis procedure for the structure break point detection. It is based on specific data pre-processing and further statistical analysis. The idea of the paper is to transform the data into a nearly monotonic function that describes the variation of machine condition or in the statistical language—change of the regime inside the process. To achieve that goal we proposed an original data processing procedure. The dataset analyzed in the paper covers one month of observation. We have received confirmation that during that period, maintenance service has been done. The purpose of our research was to remove ambiguity related to direct oil pressure analysis and visualize oil pressure variation in the diagnostic context. As a fleet of machines in the considered company covers more than 1000 loaders/trucks/drilling machines, the importance of this approach is serious from a practical point of view. We believe that it could be also an inspiration for other researchers working with industrial data.

Keywords: machine diagnostics; LHD; engine oil pressure data; oil pump wear; statistical analysis; convergence functions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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