Book Chapter10.1007/978-3-030-44610-9_30
Improving the Branded Service of Vehicles with Intelligent Driver Assistance Systems
Irina Makarova,Ksenia Shubenkova,Eduard Tsybunov,Ilsur Giniyatullin,Gleb Parsin +4 more
- 16 Oct 2019
- pp 297-305
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TL;DR: An improved algorithm for the Dealer and Service Center (DSC) operation is proposed which will minimize vehicles’ downtime while waiting for repairs and will help to predict the need for spare parts and production facilities of the DSC.
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Abstract: In the modern world, the efficiency of complex productions is possible only under the conditions of logistic, production and service systems interaction. Herewith, online data exchange between these systems is necessary in order to plan and organize all internal processes taking into account their interaction, integration and complexity. Online data on the vehicles’ state should be the basis for predicting the need for spare parts, planning their production at the manufacturer, delivery to service centers, as well as scheduling the loading of service stations. In addition, the accuracy of diagnostics and the timeliness of identifying potential failures play an important role. The existing failure prediction systems and methods are considered in the article. A systematic approach is needed to organize effective information interaction between the logistic, production and service systems. Creating an integrated information space with a common database will allow timely responding to non-standard situations and more accurately planning maintenance. The authors have proposed an improved algorithm for the Dealer and Service Center (DSC) operation which will minimize vehicles’ downtime while waiting for repairs and will help to predict the need for spare parts and production facilities of the DSC.
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References
A survey of online failure prediction methods
TL;DR: To capture the wide spectrum of approaches concerning this area, a taxonomy has been developed, whose different approaches are explained and major concepts are described in detail.
646
Predictive maintenance based on event-log analysis: a case study
TL;DR: This paper presents a general classification-based failure prediction method for target equipment types and shares insights on how to optimize the model parameters, select the most effective features, and tune classifiers to build a high-performance prediction model.
72
Random-forest-based failure prediction for hard disk drives:
TL;DR: This article proposes a failure prediction method for hard disk drives based on a part-voting random forest, which differentiates prediction of failures in a coarse-grained manner and can achieve a better prediction accuracy than state-of-the-art methods.
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Possibility of Digital Twins Technology for Improving Efficiency of the Branded Service System
Ksenia Shubenkova,Airat Valiev,Vladimir Shepelev,Sergey Tsiulin,Kristian Hegner Reinau +4 more
- 01 Nov 2018
TL;DR: In this paper, Digital Twins helps to track data of failures throughout the whole transportation and to predict failures of each particular vehicle and plan the full capacity for the final customer, and the comparison of the predicted number of failures, obtained from Digital Twin, shows a high level of flexibility that could be achieved with such a system, showing a potential towards leasing market.
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Effective techniques of FMEA at each life-cycle stage
K. Onodera
- 13 Jan 1997
TL;DR: In this article, failure mode and effects analysis (FMEA) is used in the conduct of reliability, maintainability, and safety analyses, such analyses are commonly used to identify failures of significant consequence and those affecting system performance.
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