References
[1]. Ashwani Kumar Chandel and Raj Kumar Patel (2012).
Induction Machine Bearing Fault Diagnosis With Discrete
Wavelet Transform Using Vibration Signal. i-manager's
Journal on Electrical Engineering, 5(4) Print ISSN 0973-
8835, E-ISSN 2230-7176, pp, 11-17.
[2]. A. Kazzaz, G. et.al, (2003). “Experimental
investigations on induction machine condition
monitoring and fault diagnosis using digital signal
processing techniques,” Electr. Power Syst. Res., Vol. 65,
No. 3, pp. 119–136.
[3]. D. K. M. R. B.Hulugappa, and Tajmul Pashab, (2012),
“Condition Monitoring of Induction Motor Ball Bearing
Using Monitoring Techniques,” International Journal of
Scientific and Research Publications, Vol. 2, No. 11, pp.
1–8.
[4]. F. Jiang, W. Li, Z. Wang, and Z. Zhu, (2012). “Fault
Severity Estimation of Rotating Machinery Based on
Residual Signals,” Adv. Mech. Eng., Vol. 2012, No. pp. 1–8,
[5]. Z. Chen, N. Gao, W. Sun, Q. Chen, F. Yan, X. Zhang, M.
Iftikhar, S. Liu, and Z. Ren, (2014). “A Signal Based
Triangular Structuring Element for Mathematical
Morphological Analysis and Its Application in Rolling
Element Bearing Fault Diagnosis,” Shock Vib., pp. 1–16,
[6]. B. Zhang, G. Georgoulas, M. Orchard, A. Saxena, D.
Brown, G. Vachtsevanos, and S. Liang, (2008). “Rolling
Element Bearing Feature Extraction and Anomaly
Detection Based on Vibration Monitoring,” Electr. Eng., pp.
1792–1797,
[7]. V. Muralidharan, V. Sugumaran, and V. Indira, (2014).
“Fault diagnosis of monoblock centrifugal pump using
SVM,” Eng. Sci. Technol. an Int. J., Vol. 17, No. 3, pp. 1–6.
[8]. J. Zhao, Y. Liu, J. Lou, and C. Hu, (2014). “Research of
Mechanical Fault SVM Intelligent Recognition Based on
EEMD Sample Entropy.,” Sensors & Transducers (1726- 5479), Vol. 179, No. 9, pp. 141–148,
[9]. B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi,
(2005). “Artificial neural networks and genetic algorithm
for bearing fault detection,” Soft Comput., Vol. 10, No. 3,
pp. 264–271.
[10]. Y. Y. A and C. Junsheng, (2006). “A roller bearing fault
diagnosis method based on EMD energy entropy and
ANN,” J. Sound Vib., Vol. 294, pp. 269–277,
[11]. L. Majumder and C. Manohar, (2003). “A timedomain
approach for damage detection in beam
structures using vibration data with a moving oscillator as
an excitation source,” J. Sound Vib., Vol. 268, pp.
699–716.
[12]. B. Jeon, J. Jung, B. Youn, Y. Kim, and Y. Bae, (2014),
“Statistical Approach to Diagnostic Rules for Various
Malfunctions of Journal Bearing System Using Fisher
Discriminant Analysis,” European Conference of the
Prognostics and Health Management Society 2014, pp.
1–9.
[13]. H. Khwaja, S. Gupta, and V. Kumar, (2010). “A
Statistical Approach for Fault Diagnosis in Electrical
Machines,” IETE J. Res., Vol. 56, No. 3, pp. 146–155.
[14]. S. Wu, W. Huang, F. Kong, Q. Wu, F. Zhou, and R.
Zhang, (2010). “Extracting Power Transformer Vibration
Features by a Time-Scale-Frequency Analysis Method,” J.
Electromagn. Anal. Appl., Vol. 2, No. pp. 31–38,
[15]. P. K. Kankar, S. C. Sharma, and S. P. Harsha, (2011).
“Fault diagnosis of ball bearings using continuous wavelet
transform,” Appl. Soft Comput., Vol. 11, No. 2, pp.
2300–2312.
[16]. B. Sreejith, a. K. K. Verma, and A. Srividya, (2008).
“Fault diagnosis of rolling element bearing using timedomain
features and neural networks,” IEEE International
Conference on ICIIS, No. 1, pp. 6–11.
[17]. H. M. Ertunc, H. Ocak, and C. Aliustaoglu, (2012).
“ANN- and ANFIS-based multi-staged decision algorithm
for the detection and diagnosis of bearing faults,” Neural
Comput. Appl., Vol. 22, No. S1, pp. 435–446.
[18]. P. Jayaswal, S. N. Verma, and A. K. Wadhwani,
(2010). “Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal
analysis,” J. Qual. Maint. Eng., Vol. 16, No. 2, pp. 190–213.
[19]. Y. Lei, Z. He, Y. Zi, and Q. Hu, (2006). “Fault diagnosis
of rotating machinery based on a new hybrid clustering
algorithm,” Int. J. Adv. Manuf. Technol., Vol. 35, No. 9–10, pp. 968–977.
[20]. Case Western Reserve University, (2011). “Bearing
D a t a c e n t e r ” [ o n l i n e ] , R e t r i e v e d f r o m :
URL:http://www.eecs.cwru.edu/ laboratory/ bearing/
download. html.
