Computational Systems, Inc.

Csi Technology, Inc.

Emerson Electric (US) Holding Corporation (Chile) Limitada

Other

Stewart V Bowers, Iii

Anthony John Hayzen

Kenneth Ralph Piety

Robert Darrell Skeirik

Christopher G Hilemon

Richard W Piety

William A Davis

Kai T Bouse

Raymond Earl Garvey, Iii

Joseph C Baldwin

Thomas E Nelson

Daniel L Nower

John W Willis

Todd W Reeves

Joseph A Vrba

William E Childress

Kevin D Steele

Michael D Medley

Douglas A Mann

James R Stevenson

Timothy Scott Bassett

David Q Gaddis

Bradford J Duncan

Hermann Holtmannspötter

Jim Robinson

Tuhin Banerjee

Timothy A Craven

Michael Schaffer

An apparatus is described that determines an estimated rotational speed of a rotating component of a machine in the absence of a reliable tachometer signal to indicate an actual rotational speed. The apparatus includes a processor that determines a range of rotational speeds for the machine based on historical data, generates a vibration spectrum based on measured digital vibration data, identifies vibration peaks in the spectrum, determines a maximum peak amplitude of the vibration peaks, determines candidate frequency values within the range of rotational speeds, for multiple harmonics identifies a nearest peak within a spectral peak frequency tolerance of each harmonic, determines an error value based on a difference between the candidate frequency value and the nearest peak frequency value, determines an error sum value for each candidate frequency value, which is an iterative sum of the error values determined for the harmonics, determines the estimated rotational speed of the machine based on the candidate frequency value having a largest error sum value, and analyzes the digital vibration data using the estimated rotational speed of the machine to determine an operational characteristic of the machine.

Determination of RPM based on vibration spectral plots and speed range

A vibration analyzer for use in determining a rotational speed. The vibration analyzer includes an input for sensing vibration data, a memory for storing the vibrational data, and a processor. The processor produces a spectral plot of the vibrational data, locates peaks in the spectral plot, receives an input rotational speed, and scans the spectral plot in predetermined rotational speed increments to provide a candidate rotational speeds. For each candidate rotational speed, a number of associated harmonics is identified, closest peaks in the spectral plot to the candidate rotational speed and its harmonics are located, gaps between the closest peaks and the candidate rotational speed and its harmonics are measured and summed, and a sum of the gaps is recorded. The candidate rotational speed that is associated with the maximum peak sum is selected as the nominal rotational speed.

Determination of rpm based on scanning vibration spectral plot

A statistical method is used to separate periodic from non-periodic vibration peaks in machine vibration spectra. Generally, a machine vibration spectrum is not normally distributed because the amplitudes of periodic peaks are significantly large and random relative to the generally Gaussian noise. In a normally distributed signal, the statistical parameter Kurtosis has a value of 3. The method sequentially removes each largest amplitude peak from the peaks in a frequency region of interest in the spectrum until the Kurtosis has a value of three or less. The removed peaks, which are all considered to be periodic, are placed into a candidate peak list. As the process of building the candidate peak list proceeds, if the kurtosis of the remaining peaks in the frequency region of interest falls to three or less, the process stops and the candidate peak list is defined.

System for separating periodic frequency of interest peaks from non-periodic peaks in machine vibration data

A computer implemented method processes time waveform machine vibration data that are indicative of operational characteristics of a machine. The data, which were measured on the machine over a period of time having a begin time and an end time, are accessed from a memory or storage device. An integer number M of waveform samples are determined from the data to be averaged, and an asymptotically decaying DC bias component in the data is derived using a moving average of the M number of waveform samples. The DC bias component is extrapolated from the begin time of the waveform back to an earlier time and from the end time of the waveform forward to a later time. The DC bias component is then subtracted from the time waveform data, and a Fast Fourier Transform is performed on the data to generate a spectrum.

Removal of effects of asymptotically decaying DC bias from vibration waveform

An apparatus is described that determines an estimated rotational speed of a rotating component of a machine in the absence of a reliable tachometer signal to indicate an actual rotational speed. The apparatus includes a processor that produces a spectral plot of the vibrational data, locates peaks in the spectral plot, and scans the spectral plot in predetermined rotational speed increments to provide candidate rotational speeds. For each candidate rotational speed, associated harmonics are identified, closest peaks in the spectral plot to the candidate rotational speed and its harmonics are located, gaps between the closest peaks and the candidate rotational speed and its harmonics are measured, and a sum of the gaps is recorded. The estimated rotational speed is the candidate rotational speed associated with a minimum sum of the gaps.

Determination of machine rotational speed from vibration spectral plots based on nominal speed

A vibration analyzer for use in determining a rotational speed. The vibration analyzer includes an input for sensing vibration data, a memory for storing the vibrational data, and a processor. The processor produces a spectral plot of the vibrational data, locates peaks in the spectral plot, receives an input rotational speed, and scans the spectral plot in predetermined rotational speed increments to provide a candidate rotational speeds. For each candidate rotational speed, a number of associated harmonics is identified, closest peaks in the spectral plot to the candidate rotational speed and its harmonics are located, gaps between the closest peaks and the candidate rotational speed and its harmonics are measured and summed, and a sum of the gaps is recorded. The candidate rotational speed that is associated with a minimum sum is selected as the nominal rotational speed.

Determination of rpm from vibration spectral plots based on percentage RPM

A computer-implemented method analyzes periodic information in digital vibration data associated with a machine. The method involves generating a spectral periodic information plot (PIP) based on the digital vibration data, and locating amplitude peaks in the PIP at frequencies associated with fundamental frequencies of interest. Peaks occurring at fundamental fault frequencies and at related harmonic frequencies are removed from the PIP, while retaining energy values associated with the removed peaks. Remaining peaks in the PIP are classified as synchronous periodic peaks and non-synchronous periodic peaks. The remaining peaks in the PIP are graphically plotted along with the fault frequencies and related harmonic frequencies in different colors or different line styles on a display device to identify different groups of frequencies of interest. The method implements an algorithm that locates peaks in the PIP at frequencies associated with the fundamental frequencies of interest even though frequencies of the located peaks do not precisely match the fundamental frequencies of interest.

Fault frequency matching of periodic peaks in spectral machine data

A data compression process reduces the amount of machine spectral data transmitted over a network while maintaining the details of spectral peaks used for machine health analysis. The data compression process also provides for the calculation of various types of spectral parameters, such as spectral band parameters, with negligible loss of accuracy.

Machine spectral data compression

Spectral machine condition energy peaks are graphically represented in a spectral plot using color coding, different line types, and/or filtering. This allows visual differentiation of spectral peaks associated with various fault frequency families from one another, whereby a machine condition analyst using computer-based analysis software can easily see each family of spectral peaks individually, without all the other spectral peaks, or in combinations of families that are relevant to a machine fault under investigation. In addition to current spectral data, the analyst can also view a historical trend of related scalar parameters plotted in conjunction with current spectral data, wherein the spectral data plot is synchronized with a time-based cursor on the trend plot.

Graphical differentiation of spectral frequency families

While monitoring the condition of a machine, vibration data is often collected for analysis by an experienced analyst. Systems and methods for analyzing vibration spectra associated with machine condition monitoring are disclosed herein. A system may be configured to collect vibration data from one or more vibration sensors, generate a vibration spectrum of the vibration data, and generate a spectral plot of the vibration spectrum. The system may receive a selection of a region of the spectral plot and generate a modifiable window of the vibration spectrum that is embedded within the spectral plot. The system may display a set of graphing tools along with the modifiable window that enable a user to make modifications to the window. The system may detect the modifications and update the modifiable window accordingly.

Vibration spectra window enhancement

Determination of machine rotational speed based on vibration spectral plots

A statistical method is used to separate periodic from non-periodic vibration peaks in autocorrelation spectra. Generally, an autocorrelation spectrum is not normally distributed because the amplitudes of periodic peaks are significantly large and random relative to the generally Gaussian noise. In a normally distributed signal, the statistical parameter kurtosis has a value of 3. The method sequentially removes each largest amplitude peak from the peaks in the spectrum until the kurtosis is 3 or less. The removed peaks, which are all considered to be periodic, are placed into a set. The total energy of the peaks in the set is considered to be the total periodic energy of the spectrum. As the process of building the peak set proceeds, if its total energy becomes greater than or equal to a predefined energy threshold before its kurtosis reaches 3 or less, the process stops and the periodic peak set is defined.

System for separating periodic amplitude peaks from non-periodic amplitude peaks in machine vibration data

A vibration analyzer for use in determining a rotational speed. The vibration analyzer includes: a) an input for sensing vibration data, b) a memory for storing the vibrational data, and c) a processor. The processor 1) produces a spectral plot of the vibrational data, 2) locates peaks in the spectral plot, 3) inputs a rotational speed, and 4) scans the spectral plot in predetermined rotational speed increments to provide a candidate rotational speeds. For each candidate rotational speed i) a number of associated harmonics is identified, ii) closest peaks in the spectral plot to the candidate rotational speed and its harmonics are located, iii) gaps between the closest peaks and the candidate rotational speed and its harmonics are measured, iv) summed, and a sum of the gaps is recorded. In step (5) The candidate rotational speed that is associated with a minimum sum is selected as the nominal rotational speed.

Determination of RPM from vibration spectral plots

Vibration data indicative of the health of a machine is collected using a vibration sensor connected to a portable vibration data collector. After the vibration sensor has been attached to a measurement point on the machine, vibration data is collected over a measurement time period having a begin time and an end time, and the vibration data is stored in memory of the portable vibration data collector. First and second average amplitudes of the vibration data collected during first and second time windows in the measurement time period are determined. The slope of the vibration data is calculated based on the ratio of the amplitude difference between the first and second average amplitudes and the time difference between the first and second time windows. The vibration data is either retained in the memory or discarded based on the comparison of the slope to a threshold level.

Detecting faulty collection of vibration data

Vibration data indicative of the health of a machine is collected using a vibration sensor connected to a vibration data collector. After the vibration sensor has been attached to a measurement point on the machine, vibration data is collected that includes a bin of data having a begin time and an end time, and the vibration data is stored in memory of the vibration data collector. First and second average amplitudes of the bin of vibration data collected during first and second time windows in the measurement time period are determined. The slope of the vibration data is calculated based on the ratio of the amplitude difference between the first and second average amplitudes and the time difference between the first and second time windows. The vibration data is either retained in the memory or discarded based on the comparison of the slope to a threshold level.

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Knoxville, TN, United States of America

Stewart V Bowers, Iii