Analysis of Fleet Data Using Machine Learning Methods

doi:10.11908/j.issn.0253-374x.22735

Home > Archive>Volume 49, Issue S1, 2021 >186-193. DOI:10.11908/j.issn.0253-374x.22735

Analysis of Fleet Data Using Machine Learning Methods
DOI:
                        10.11908/j.issn.0253-374x.22735
                    
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                        [cstr]
                    
Author:
                        EBEL André1EBEL André
Research Institute of Automotive Engineering and Vehicle Engines Stuttgart （FKFS）， 70569 Stuttgart， Germany
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RIEMER Thomas1RIEMER Thomas
Research Institute of Automotive Engineering and Vehicle Engines Stuttgart （FKFS）， 70569 Stuttgart， Germany
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REUSS Hans-Christian2REUSS Hans-Christian
Institute of Automotive Engineering （IFS）， University of Stuttgart， 70569 Stuttgart， Germany
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Affiliation:1.Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS)， 70569 Stuttgart， Germany;2.Institute of Automotive Engineering (IFS)， University of Stuttgart， 70569 Stuttgart， Germany
Clc Number:U461
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Abstract:

To enhance the functions and improve the safety of the new generation of vehicles， this paper collected abundant history data of vehicles and then created a rule-based model by using machine learning methods， so as to detect the faulty vehicle in a fleet. Several steps were designed for detailed illustration， and the validation of the method was conducted through electrical fault of the LV （lithium-cobalt） battery. The results can be used as input for the test bench tests of the following vehicle generations.

Key words:fleet data;machine learning;rule learning;test conditions

Reference

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EBEL André,RIEMER Thomas, REUSS Hans-Christian. Analysis of Fleet Data Using Machine Learning Methods[J].同济大学学报(自然科学版),2021,49(S1):186~193

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Received:August 30,2021
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Online: February 28,2023
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