Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Single Mode Negative Dispersion Hexagonal Photonic Crystal Fiber Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Effective Method for Maintenance Scheduling of Vehicles Using Neural Network

Authors : Sushma Kamlu, Vijaya Laxmi

Published in: Proceedings of the International Conference on Microelectronics, Computing & Communication Systems

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The maintenance scheduling of vehicles of a transportation system has its own significance as far as effective operation of a transportation system is concerned. Presently, inspection planning is used to plan for maintenance activity of vehicles in a transportation system. It helps the operator to organize maintenance activity and increase the ability to identify a proactive failure situation. In order to avoid the dilemma like premature aging and failure of vehicles in transportation system responsible for spontaneous and costly maintenance charges, at regular intervals, it is imperative to carry out preventive maintenance (PM). This paper presents the application of neural network technique for automatic maintenance scheduling of vehicles. This paper presents an economic method for solving maintenance scheduling of medium type vehicles by exploiting the neural network technique.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Fabrication and Investigation of Low-Voltage Programmable Flash Memory Gate Stack
next chapter Improved Clustering for Categorical Data with Genetic Algorithm
Literature
1.
2.
R.-M. Chen, Y.-M. Huang, Competitive neural network to solve scheduling problems. Neurocomputing 37, 177–196 (2001)CrossRefMATH
3.
J.G. Park, J.M. Park, D.S. Kim, C.H. Lee, S.W. Suh, M.S. Han, Dynamic neural network with heuristic. IEEE Int. Conf. Neural Networks 7, 4650–4654 (1994)
4.
R.C. Correa, A. Ferreira, P. Rebreyend, Scheduling multiprocessor tasks with genetic algorithms. IEEE Trans. Parallel Distrib. Syst. 10(8), 825–837 (1999)
5.
T.M. Willems, J.E. Rooda, Neural networks for job-shop scheduling. Control Eng. Pract. 2, 31–39 (1994)CrossRef
6.
T.C.T. Du, P.M. Wolfe, Implementation of fuzzy logic systems and neural networks in industry. Comput. Ind. 32(3), 261–272 (1997)
7.
J.A. Stegemann, N.R. Buenfeld, A glossary of basic neural network terminology for regression problems. Neural Comput. Appl. 8(4), 290–296 (1999)CrossRef
8.
S.K. Lahiri, K.C. Ghanta, Artificial neural network model with parameter tuning assisted by genetic algorithm technique: study of critical velocity of slurry flow in pipeline. Asia-Pacific J. Chem. Eng. 5(5), Sept/Oct 2010, 763–777 (2009)
9.
O. Slaymaker, The role of remote sensing in geomorphology and terrain analysis in the Canadian cordillera. Int. J. Appl. Earth Obs. Geoinf. 3(1), 11–17 (2001)
10.
N.N. Clark, J.M. Kern, C.M. Atkinson, R.D. Nine, Factors affecting heavy-duty diesel vehicle emissions. J. Air Waste Manage. Assoc. 52(1), 84–94 (2002). ISSN 1047-3289
11.
12.
K.F. Reinschmidt, Neural networks: next step for simulation and control. Power Eng. (United States). 95(11) (1991). OSTI ID: 5281949
13.
O. Aloquili, A. Elbanna, and A. Al-Azizi, Automatic vehicle location tracking system based on GIS environment. IET. Softw. 3(4), 255–263 (2009)
Metadata
Title
An Effective Method for Maintenance Scheduling of Vehicles Using Neural Network
Authors
Sushma Kamlu
Vijaya Laxmi
Copyright Year
2018
Publisher
Springer Singapore
Book
Proceedings of the International Conference on Microelectronics, Computing & Communication Systems

Print ISBN: 978-981-10-5564-5

Electronic ISBN: 978-981-10-5565-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-5565-2_5