"Forecast" - adaptive forecast diagnostic intelligent system for vehicles. Issue 1 (April 2020)

• Record Type: Journal Article
• Authors: Poddubnaya, A A; Keller, A V
• Is Part Of: IOP conference series. Volume 819:Issue 1(2020)
• Publication Date: 2020-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/819/1/012008
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The article contains general information about promising vehicle diagnostic systems. Existing diagnostic systems, including those built into modern vehicles (MV), are not able to predict the moment of failure of components and assemblies, but only state the fact of a malfunction. To diagnose the current state and predict the residual life of the vehicle in motion mode, a mathematical model, based on machine learning technologies and data from standard additional sensors and MV detectors, is suggested. Using this approach will make it possible to forecast the defect before its actual occurrence.