Template for submission of Survey papers for Literature survey (PaperCode)

First A. Author, Second B. Author, Jr., and Third C. Author

F. A. Author, Paper Name, Department of Computational Science, University, India ( e-mail: author1@ xyz.com).

S. A. Author, Paper Name, Department of Computational Science,University, India ( e-mail: author2@ xyz.com).

T. A. Author, Paper Name, Department of Computational Science, University, India ( e-mail: author3@ xyz.com).

ABSTRACT

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Keywords:

About six to fifteen key words or phrases in alphabetical order, separated by commas. For a list of suggested keywords, visit

1. INTRODUCTION

2. Section two

2.1 Sub-section Head Style

2.2 Sub-section Head Style

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2.3 Sub-section Head Style

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2.3.1 Sub-section Head Style


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2.3.2 Sub-section Head Style

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2.4 Sub-section Head Style

3. Section Head

4. Section Head

5. Section head

5.1 Sub-section Head Style

5.2 Sub-section Head Style

5.2.1 Sub-sub-Section Head Style

5.3 Sub-sub Section Head Style

Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections.

Be aware of the different meanings of the homophones “affect” (usually a verb) and “effect” (usually a noun), “complement” and “compliment,” “discreet” and “discrete,” “principal” (e.g., “principal investigator”) and “principle” (e.g., “principle of measurement”). Do not confuse “imply” and “infer.”

Prefixes such as “non,” “sub,” “micro,” “multi,” and “ultra” are not independent words; they should be joined to the words they modify, usually without a hyphen. There is no period after the “et” in the Latin abbreviation “et al.” (it is also italicized). The abbreviation “i.e.,” means “that is,” and the abbreviation “e.g.,” means “for example” (these abbreviations are not italicized).

Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections.

6. Section Head

7. Section Head

Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections. Fill the text from your manuscript in different sections.

8. Conclusion

A conclusion section is not required. Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions.

Appendix

Appendixes, if needed, appear before the acknowledgment.

Acknowledgment

References

1. R. Roy, and T. Kailath, “ESPRIT _ estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust. Speech Signal Process, Vol. 37, no. 7, pp. 984_95, Jul. 1989.

2. V. Wowk, Machinery Vibration, Measurement and Analysis. New York: McGraw-Hill, 1991.

3. Case Western Reserve University Bearing Data Center. Available: http://csegroups.case.edu/bearingdatacenter/pages/downloaddata-file, accessed Apr. 2013.

4. B. Li, M.-Y. Chow, Y. Tipsuwan, and J. C. Hung, “Neural network based motor rolling bearing fault diagnosis,” IEEE Trans. Ind. Electron.,Vol. 47, no. 5, pp. 1060_9, Oct. 2000.

5. J. Altmann, and J. Mathew, “Multiple band-pass autoregressive demodulation for rolling element bearing fault diagnosis,” Mech. Syst. Signal Process., Vol. 15, no. 5, pp. 963_77, Sep. 2001.

6. B. Samanta, and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time domain features,” Mech. Syst. Signal Process., Vol. 17, no. 2, pp. 317_28, Mar. 2003.

7. B. S. Yang, T. Han, and J. L. An, “ART_KOHONEN neural network for fault diagnosis of rotating machinery,” Mech. Syst. Signal Process., Vol. 18, no. 3, pp. 645_57, May 2004.

8. L. Zhang, L. B. Jack, and A. K. Nandi, “Fault detection using genetic programming,” Mech. Syst. Signal Process., Vol. 19, no. 2, pp. 271_89, Mar. 2005.

9. A. Saxena, and A. Saad, “Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems,” Appl. Soft Computer., Vol. 7, no. 1, pp. 441_54, Jan. 2007.

10. B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. Orchard, and G. Vachtsevanos, “A probabilistic fault detection approach: Application to bearing fault detection,” IEEE Trans. Ind. Electron., Vol. 58, no. 5,

pp. 2011_8, May 2011.

11. Y. Jianbo, “Local and nonlocal preserving projection for bearing defect classification and performance assessment,” IEEE Trans. Ind. Electron., Vol. 59, no. 5, pp. 2363_76, May 2012.

12. M. D. Prieto, G. Cirrincione, A. G Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks,”IEEE Trans. Ind. Electron., Vol. 60, no. 8, pp. 3398_407, Aug. 2013.

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Template of Survey papers