PeakVue Analysis for Antifriction Bearing Fault Detection - Ball or Roller Defect
PeakVue analysis, introduced by Emerson, is an effective tool for identifying bearing defects by analyzing peak values, spectra, and autocorrelation coefficients of impact-like events. This methodology uses a high-pass filter and focuses on peak values rather than low-pass filtering and spectral analysis. Case studies demonstrate its effectiveness in identifying various bearing defects.
PeakVue Analysis for Antifriction Bearing Fault Detection
Peak values (PeakVue) are observed over sequential discrete time intervals, captured, and analyzed. The analyses are the (a) peak values (measured in g’s), (b) spectra computed from the peak value time waveform, and (c) the autocorrelation coefficient computed from the peak value time waveform.
Case studies of various classes of faults are presented to illustrate the PeakVue methodology. The classes of faults are (a) inner race defects, (b) outer race defects, (c) rolling element defects, and (d) cage related defects. All three analysis tools enable the identification of the defect and often the severity of the defect.
Introduction
The peak value analysis (PeakVue) methodology introduced by Emerson for the analysis of impact-like events is proven to be an effective tool for identifying bearing defects.
Overview of Signal Processing for Vibration Analysis
The analog signal from the vibration sensor is generally routed through some analog signal processing, converted into a digital format and then further processed digitally. The vibration sensor often is an accelerometer whose output is expressed in g units.
The signal processing may include conversion of the signal from acceleration to velocity units employing an analog integrator. The analog signal (g or velocity units) generally is passed through a high order low pass filter immediately before the analog-to-digital converter to remove any signal components which may be present at frequencies greater than the Nyquist frequency defined as one half of the sampling rate.
This provides assurance that the digital representation of the analog signal is correct, i.e., the band limited analog signal existing prior to digital conversion could be reconstructed from the digital signal. Once a block of digital data is acquired at a constant sampling rate of desired length, typically a block size of 2n where n is an integer, the digital data are further processed. By far the most common processing for analyzing rotating equipment is the Fourier Transform, using a FFT algorithm to construct the spectrum either in acceleration or velocity units. The spectral analysis is helpful in separating the band-limited signal into periodic components related to the turning speed of the machine.
In addition to spectral analysis, auto-correlation analysis can be applied to the digital block of data representing the time waveform.
These additional correlation analyses have not proven to be helpful to the normal spectral analysis, but it can be beneficial for analysis of time waveform acquired when employing PeakVue analysis.
In PeakVue analysis, no low pass filter at or slightly below the Nyquist frequency is employed. Instead, a high pass (or band pass) filter greater than or equal to the nyquist frequency is employed. The digital block of data consists of absolute maximum values, which the time waveform experiences over each time increment defined by the sampling rate. Hence the analysis of this representative time waveform is the analysis of peak values.
The analysis of this block of data consists of the peak values themselves and an identification of periodicity that is best accomplished
using spectral analysis. The autocorrelation analysis has also been found to be very beneficial for the peak value time waveform.
Additional Images
