Application of High Frequency Acceleration Envelope Power Spectrum and ANN for Fault Diagnosis on Journal Bearing Under Various Loading Conditions


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Journal bearings are widely used to support the shaft of industrial machinery with heavy loads, such as compressors, turbines and centrifugal pumps. The major problem in journal bearing is catastrophic failure due to corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. High frequency acceleration enveloping facilitates the extraction of low amplitude, high frequency signals associated with repetitive impacts in journal bearings, providing a key tool for early detection in the onset of bearing damage and similar machinery health problems when coupled with standard FFT analysis. The DEWESOFT software-based methods for implementing and interpreting high frequency acceleration enveloping are presented and compared. In this paper the application of STFT (Short Time Fourier Transform) and Autocorrelation through FFT are used for processing vibration signal to detect faults in journal bearing is presented. A bearing testing apparatus is used for experimental studies to obtain vibration signal from a healthy bearing and fault bearing. Further application of ANN for extracting features like RMS, mean, kurtosis, shape factor, crest factor and impulse factor is investigated as a model for automated fault diagnosis is presented.


Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Journal Bearing Power Spectrum; Vibration; Condition Monitoring; ANN

Full Text:

PDF


References


T. Narendiranath Babu, T. Manvel Raj, T.Lakshmanan, Application of Butterworth Filter for Fault Diagnosis on Journal Bearing, Journal of VibroEngineering, Vol. 16., Issue 3, pp. 1602-1617, 2014.

Rajendran, V. Engineering Physics. Tata McGraw-Hill Education, 2009

Ugural. A. C, Mechanical design: an integrated approach. (McGraw Hill Professional, 2003).

Parno. R, Al-Thobiani. F, Gu. Fengshou, Andrew. Ball, (2010). Early failure detection and diagnostics of high speed self aligning journal bearing. COMADEM, Nara, Japan, June 28-July 2.

Girdhar, Paresh , Scheffer, Cornelius Practical machinery vibration analysis and predictive maintenance. (Elsevier, 2004).

Lee. Jay, Computer-aided maintenance: methodologies and practices. (Springer, 1999).

De Castro, H.F, Cavalca, Lucchesi. K, Rainer. N. Whirl and whipinstabilities in rotor- bearing system considering a nonlinear force model, Journal of Sound and Vibration, Vol. 317, Issues 1-2, pp. 273-293, 2008.

Taylor. I, Scatchard. D More monitoring – less downtime. World pumps, Vol. 2008, Issues 507, pp. 36-37, 2008.

Bannister, R. H, Findlay, G. E, Condition monitoring of low-speed rotating machinery using stress waves, part 1. Proceedings of the Institution of Mechanical Engineers, part E: Journal of Process Mechanical Engineering, Vol. 213, No. 3, pp. 153-170.

Kuboyama. K, (1997) Development of low speed bearing diagnosis technique.Fukuyama City, Japan,.

SKF. Vibration diagnostic guide., (SKF Condition Monitoring 2000).

Rafieea.J,:Arvania.F,:Harifib.A, and Sadeghic.M.H Intelligent condition monitoring of a gearbox using artificial neural network; Mechanical systems and signal processing; 21, pp 1746-1754, 2007.

Xiao, H., Wang, X., A review of piezoelectric vibration energy harvesting techniques, (2014) International Review of Mechanical Engineering (IREME), 8 (3), pp. 609-620.

Ravindra Prasad, R., Palani, P.K., Active vibration control in smart structures, (2014) International Review of Mechanical Engineering (IREME), 8 (2), pp. 379-386.

Yunoh, M.F.M., Abdullah, S., Nopiah, Z.M., Nuawi, M.Z., A review of localised time-frequency features classification associated to fatigue data analysis, (2013) International Review of Mechanical Engineering (IREME), 7 (5), pp. 960-976.

Mahesh, G., Muthu, S., Devadasan, S.R., Experimentation and prediction of vibration amplitude in end milling with reference to radial rake angle, (2012) International Review of Mechanical Engineering (IREME), 6 (6), pp. 1164-1174.

El Kadi, H.A., Deiab, I.M., Khattab, A.A., On real time prediction of cutting forces using ANN, (2014) International Review of Mechanical Engineering (IREME), 8 (1), pp. 197-208.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize