Intelligent Maintenance System Integrating Support Vector Machine, Isometric Mapping, Genetic Algorithm and RFID Technology

Zhi Xin Yang; Chen Lei

doi:10.4028/www.scientific.net/AMM.536-537.454

Paper Titles

Spectrum Prediction for Cognitive Radio System Based on Optimally Pruned Extreme Learning Machine
p.430

Research of Early-Warning Expert System for Security of Grain Storage Based on Uncertain Inference
p.437

A Method of Fast Weighted Evidence Combination Exhibited by Compound Conflict
p.443

Feature Extraction for Kernel Minimum Squared Error by Sparsity Shrinkage
p.450

Intelligent Maintenance System Integrating Support Vector Machine, Isometric Mapping, Genetic Algorithm and RFID Technology
p.454

Research of In-Car Navigation Based on Improved Ant Colony Algorithm
p.461

The Application of Immune Genetic Algorithm in Parallel Sorting
p.466

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network
p.470

Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications
p.476

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 536-537Intelligent Maintenance System Integrating Support...

Intelligent Maintenance System Integrating Support Vector Machine, Isometric Mapping, Genetic Algorithm and RFID Technology

Abstract:

With the emerging of RFID technology and increasing pressure on maintenance, higher request is posed on the maintenance action. This paper introduces a combined intelligent system to complete the maintenance task. SVM and SVR model has been trained to classify machine fault types and predict the degradation. The proposed system can carry out maintenance action with the staff position information form RFID tags and the machine condition information. Genetic algorithm will be used to search the best maintenance sequence, then, the combined information will help make most efficient maintenance decision.

You might also be interested in these eBooks

Advances in Mechatronics, Robotics and Automation II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 536-537)

Pages:

454-460

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.536-537.454

Citation:

Cite this paper

Online since:

April 2014

Authors:

Zhi Xin Yang, Chen Lei*

Keywords:

Condition Based Maintenance, Indoor Positioning, RFID, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] N. S. Arunraj and J. Maiti, Risk-based maintenance—Techniques and applications, Journal of Hazardous Materials, vol. 142, pp.653-661, (2007).

DOI: 10.1016/j.jhazmat.2006.06.069

Google Scholar

[2] F. K. Geitner and H. P. Bloch, Machinery Failure Analysis and Troubleshooting (Fourth Edition), in Machinery Failure Analysis and Troubleshooting (Fourth Edition), ed Oxford: Butterworth-Heinemann, (2012).

DOI: 10.1016/b978-0-12-386045-3.00004-0

Google Scholar

[3] V. T. Tran, et al., Machine condition prognosis based on regression trees and one-step-ahead prediction, Mechanical Systems and Signal Processing, vol. 22, pp.1179-1193, (2008).

DOI: 10.1016/j.ymssp.2007.11.012

Google Scholar

[4] H. T. Pham and B. -S. Yang, Estimation and forecasting of machine health condition using ARMA/GARCH model, Mechanical Systems and Signal Processing, vol. 24, pp.546-558, (2010).

DOI: 10.1016/j.ymssp.2009.08.004

Google Scholar

[5] W. Q. Wang, et al., Prognosis of machine health condition using neuro-fuzzy systems, Mechanical Systems and Signal Processing, vol. 18, pp.813-831, (2004).

DOI: 10.1016/s0888-3270(03)00079-7

Google Scholar

[6] P. Wang and G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks, Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, vol. 15, pp.349-365, (2001).

DOI: 10.1017/s0890060401154089

Google Scholar

[7] R. C. M. Yam, et al., Intelligent predictive decision support system for condition-based maintenance, International Journal of Advanced Manufacturing Technology, vol. 17, pp.383-391, (2001).

DOI: 10.1007/s001700170173

Google Scholar

[8] S. Zhang and R. Ganesan, Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery, Journal of Engineering for Gas Turbines and Power, vol. 119, pp.378-384, (1997).

DOI: 10.1115/1.2815585

Google Scholar