Author Topic: A Wavelet based multiresolution analysis for real time condition monitoring of A  (Read 2679 times)

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Author : Subhra Debdas, M.F.Qureshi, A.Reddy, D.Chandrakar, D.Pansari
International Journal of Scientific & Engineering Research Volume 2, Issue 10, October-2011
ISSN 2229-5518
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Abstract-Wavelet is a powerful tool used for non stationary signal analysis. It does not change the time information content present in the signal hence it provides a time-frequency representation of the signal. Using the wavelet technique, transients can be decomposed into series of wavelet components, in which each is a time-domain signal that covers a specific frequency band. Disturbances of small intervals are amplified frequency band. In this paper a multi-resolution based pattern recognition technique is used for vibration analysis of angle grinder machine by which different frequencies are analyzed with different resolutions. This method is more reliable as compared to other FFT based techniquesrelative to the rest of the signal when projected to similar size wavelet bases and, thus, they can be easily detected in the corresponding

          Index Terms- Fault diagnosis, wavelet transform, multi resolution analysis, pattern recognition, wavelet density estimation.

1      INTRODUCTION                                                         
Condition monitoring is the process for monitoring any parameter of condition in machinery, such that a significant change is indicative of a developing failure. The use of condition monitoring allows maintenance to be scheduled, or other necessary actions to be taken to avoid the consequences of failure, before the failure occurs [1]. Nevertheless, a deviation from a reference value must occur to identify impeding damages in the machinery. Condition monitoring for any machine is much more cost effective than allowing the machinery to fail. The prime aim of vibration monitoring is the detection of changes in the vibration condition of the machine under investigation during its operation.
In industry machines are expected to run continuously with their full capacity in order to meet the production needs. Any defects in the machinery must be detected and should be analyzed at the early stage to avoid its failure. In this case planned shutdown can be arranged to diagnose the causes of the problem and to make further corrections. In opposite to this condition, the unscheduled shut down of the machinery & equipment can cause enormous economic losses and may result high damage of the machine. So condition monitoring of various machines is gaining importance in every industry since it keeps the plant at healthy condition for maximum production, detecting and diagnosing the fault at very early stage to avoid serious accidents and machine damage and to run the plant economically [2].Most of the defects occurred in the machines give rise to a distinct vibration signature and hence mostly faults can be identified using vibration signature analysis techniques. A similar attempt have been tried out here for the conditioning monitoring of AC machine using wavelet technique for its proper and economical functioning[3]. An AC machine is a handheld power tool used for cutting, grinding and polishing. Angle grinders may be used both for removing excess material from a piece or simply cutting into a piece.AC machines are widely used in metalworking and construction, as well as in emergency rescues. They are commonly found in workshops, service garages and auto body repairs.

1.1   From Fourier Analysis to Wavelet Analysis

Drawbacks of signal processing techniques used in power quality disturbances:
i) RMS is major tool used in signal processing techniques. The RMS of signal is not an analysis technique but it gives some basic information about an electrical system. The main disadvantages of this algorithm is its dependence on size of sample window[6].As a result of small window RMS parameter becomes less relevant and loses meaning of mean value of power.
ii) Another most widely used tool in signal processing is Fourier analysis. It helps in analysis of harmonics and essential tool for filter design. The DFT and FFT are essential tools for estimation of fundamental amplitude of signal. The DFT importance in area of frequency (spectrum) analysis as it takes a discrete signal in time domain and transforms that signal into the discrete frequency domain representation. A FFT used for transformation of signal from time domain to frequency domain. Speed is main advantage of this technique and also high speed calculations.
iii) In time frequency signal processing, a filter banks is special quadric time frequency distortion (TFD) that represents signal in joint time frequency domain. This technique used for estimation of specific sub-band components.
iv) Another special type of filter is Kalman Filter .Their solu-tions are based on set of state space equations. These are used for real time tracking harmonics as proposed in [8], frequency estimation under distorted signal [9], estimating voltage and current parameters on power system protection and parameter of transient [10].
v) In 1994, use of wavelets was proposed which led to study of non stationary harmonic distortion in power systems. This technique decomposes signals in different frequency sub-bands and characteristics can be studied separately.

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