REVIEW ARTICLE
Research on the Characteristic of Automotive Failure Diagnosis Based on Complex Networks
Lei Tongfei*, Li wei, Wang Jianfeng, Zhao Jinguo
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 508
Last Page: 513
Publisher Id: TOMEJ-9-508
DOI: 10.2174/1874155X01509010508
Article History:
Received Date: 17/2/2014Revision Received Date: 21/3/2015
Acceptance Date: 9/6/2015
Electronic publication date: 24/8/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In order to research the mechanism of automotive failure diagnose and to improve, as well as to explore a new perspective to a find automotive failure diagnose quickly. This paper is based on the empirical data to analyze Xian’s some 4S shop and its self-organized criticality proposed a new suggestion. In this paper, we analyze in depth the data of automotive failure running status and diagnose index of different period between 2014, based on the theory of automotive failure diagnosed complexity and self-organized criticality, and thus proves the characteristics of power-law under which lies the related scale. The result shows us that, automotive failure diagnose system is a dynamical system that’s both extensive and dissipative. In addition, when STATUS is under 20 or less and TPI is above 6, the scale of influenced districts caused by index in automotive diagnose system and the related frequency fits the law-power distribution, and the rising of automotive will reach the state of self-organized criticality, and meets the characteristic of self-organized criticality.
