Enveloping and PeakVue - Research Paper Example

Due to its high precision, the method is able to detect the emission of stress waves caused by direct contact of adjacent metallic surfaces at an early stage. Generally, through spectral analysis the frequency of repetition of the stress waves is availed. By peakvue, the resonance zones are isolated through use of filters. Capturing the peak values for particular intervals of the selected sampling time is done through the application of high frequency clustering of signals with over one hundred kilo Hertz.

The method goes through four stages The initial stage in which the low frequency signals are eliminated by taking the entire signal through a high pass filter. The amplitudes are run through the accelerometer which detects the measurements and is able to classify them according to the initially specified cut-off frequency level. All the measurements reading below the designated cut-off level are classified together. They are effectively eliminated which implies that only the high frequency readings- those above the cut-off level, proceed to the second phase of the analysis procedure.

By elimination it means that such values are truncated from the recorded measurements such that their consideration ends at that phase. The second phase that involves the digital conversion of frequency. The high frequency signal is changed from its initial analog form to digital form in order to begin the spectral 0246analysis. Normally, high frequency values will be recorded for particular sections along the surface of the bearing. Therefore whenever the metallic surfaces come into contact during the rotation, the frequencies hit a peak.

If special readings were availed at phase two for any specific time duration, it will be analyzed here. If for specified time duration the amplitude levels of the converted signals read beyond a predefined threshold it is then matched to a digital value. The essence of this matching is that it is not always possible to produce signals with equal frequencies, even when the components are running on a fairly flat or even ground. This could be the result of instantaneous change in the position of the load, continued distribution of the lubricant along the colliding surfaces, and varying positioning of the emerging fault.

Therefore, the simplest way to conclude that the frequencies were derived from a ‘certain’ spot along the surface of the bearing is to cluster all measurements within a certain range. These measurements clustered together are then assigned a specific digital value, which identifies them together. The display is rendered once the digital value corresponding to the particular time interval is processed using the Fast Fourier Transform (FFT) algorithm. The FFT algorithm works out the Discreet Fourier Transform and the corresponding inverses.

To obtain the Discrete Fourier Transform, a sequence of amplitude values is decomposed to form components of varying frequencies. The frequencies so formed are categorized according to their closeness, that is, according to pre-determined intervals. The classification is done with reference to how often a specific range acquires numerical frequency. For example if the outlier frequency is too scarce, the measurements thus classified can be overlooked and ignored. If there is higher consistency in the numerical frequency of measurements with