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Abstract
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Article Information:
A Real Time Data Acquisition and Vibration Analysis through Wavelet Transform for Fault Detection of Industrial Drives
K. Jayakumar and S.Thangavel
Corresponding Author: K. Jayakumar
Submitted: September 13, 2014
Accepted: September 20, 2014
Published: January 15, 2015 |
Abstract:
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Prevention is better than cure is not only applicable to medical domain but also equally applicable to engineering field. By analyzing the symptoms and taking appropriate corrective measures before the actual failure takes place would avoid costly cateroscopic damages. In line with this approach a fault analysis through vibration study with wavelet transforms has been attempted in this study. The previous researchers adopted a wavelet analysis for stator fault, such as stator current signature analysis. But the present study had adopted frequency pattern using the decomposition of wavelet pocket for induction machine fault analysis. Subsequently the machine would be determined by analyzing the data obtained through HAAR wavelet. Induction motor, one with good condition and another with two broken ball bearing were taken up for fault analysis using above techniques. The proposed investigation of vibration analysis is done through frequency pattern using the decomposition of wavelet packet. The wavelet approximate and detailed coefficients for the vibration signal have been extracted over a wide range and the analysis could be percolated on the frequency domain. In this study, healthy and unhealthy motor are used to experimentally observe the vibration signals are transformed into power spectral density through Matlab tools and the steady state rotor frequency was introduce with a new frequency pattern for fault diagnosis.
Key words: Broken ball bearing, decomposition level, frequency, fault diagnosis, HAAR wavelet, Induction Machines (IM), power spectrum, vibration, Wavelet Transform (WT)
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Cite this Reference:
K. Jayakumar and S.Thangavel, . A Real Time Data Acquisition and Vibration Analysis through Wavelet Transform for Fault Detection of Industrial Drives. Research Journal of Applied Sciences, Engineering and Technology, (2): 122-128.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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