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Improved Mechanical Fault Identification of an Induction Motor Using Teager-Kaiser Energy Operator
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논문 기본 정보

Type
Academic journal
Author
Sudhir Agrawal (Madan Mohan Malaviya University of Technology) V. K. Giri (Madan Mohan Malaviya University of Technology)
Journal
The Korean Institute of Electrical Engineers Journal of Electrical Engineering & Technology Vol.12 No.5 KCI Accredited Journals SCIE
Published
2017.9
Pages
1,955 - 1,962 (8page)

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Improved Mechanical Fault Identification of an Induction Motor Using Teager-Kaiser Energy Operator
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Abstract· Keywords

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Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.
#Bearing Damage Index (BDI) #Kurtosis #Power Spectral Density(PSD) #Skewness #Teager-Kaiser Energy Operator (TKEO)

Contents

Abstract
1. Introduction
2. Theoretical Background
3. Methodology
4. Results and Discussions
5. Conclusions
References

References (22)

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A. K. S. Jardine, D. Lin, and D. Banjevic, “A Review on Mchinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483-1510, Oct. 2006. S. A. Mortazavizadeh and S. M. G. Mousavi, “A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines,” Phys. Sci. Int. J., vol. 4, no. 3, pp. 310-338, 2014. G. K. Singh, A. Saleh, and A. Kazzaz, “Induction Machine Drive Condition Monitoring and Diagnostic Research - A Survey,” Electr. Power Syst. Res., vol. 64, no. 2, pp. 145-158, 2003. M. Tsypkin, “Induction Motor Condition Monitoring: Vibration Analysis Technique - a Practical Implementation,” in 2011 IEEE International Electric Machines & Drives Conference, 2011, pp. 406-411. D.-H. Hwang, Y.-W. Youn, J.-H. Sun, K.-H. Choi, J.-H. Lee, and Y.-H. Kim, “Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals,” J. Electr. Eng. Technol., vol. 10, no. 4, pp. 1558-1565, 2015.

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