PSI - Issue 59

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 59 (2024) 436–443

VII International Conference “In -service Damage of Materials: Diagnostics and Prediction ” (DMDP 2023) Extension of the service term of the resource-determining elements of vehicle units based on the artificial neural network model of their defects Victor Aulin а , *, Oleg Lyashuk b , Serhii Lysenko a , Oleg Tson b , Andrii Hrynkiv a , Nataliia Rozhko b Abstract The article shows the processing of diagnostic information of the resource-determining elements of the YMZ-238 engine: the cylinder-piston group, the slider-crank linkage, and the valvetrain, which characterizes their technical condition using IBM SPSS Statistics software. The technique of the maximum and minimum value of the criterion sample of Tytien Mur has been developed. The value of errors in recognising combinations of motor element defects depending on the number of neurons in the hidden layer was determined. The graphic representation of the convergence of the recognized defects of the resource-determining elements (the cylinder-piston group, the slider crank linkage, the valvetrain) of the engine during training and testing by the artificial neural network method is shown. The results of the combination of defects for the resource-determining elements of the engine were formed in the form of a matrix of technical states and graph models of an artificial neural network. Their changes with the reduction of defect recognition errors indicate the extension of the service life of the elements of car units. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2023 Organizers © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2023 Organizers а Central Ukrainian National Technical University, 25006 Kropyvnytskiy, Ukraine b Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine

* Corresponding author. Tel.: +380988997104. E-mail address: Aulinvv@gmail.com

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2023 Organizers

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of DMDP 2023 Organizers 10.1016/j.prostr.2024.04.062