Computational Systems, Inc.

Csi Technology, Inc.

Anthony John Hayzen

Christopher G Hilemon

Stewart V Bowers, Iii

Trevor Duncan Schleiss

John W Willis

Manikandan Janardhanan

William L Sells

Deane M Horn

Raymond Earl Garvey, Iii

Michael D Rich

Andrew Carey

Kenneth Ralph Piety

Thomas E Nelson

David L Bass

Raymond E Garvey

Michael D Medley

Kevin D Steele

Mark L Granger

Viva C Williams

Jim W Walker

Robert Darrell Skeirik

Joseph A Vrba

Kai T Bouse

Bradley D Pardue

Anthony Grahame Fogel

Mark Slemp

A vibration data collection device monitors vibration of a machine, generates original machine vibration waveform data based on the monitored vibration, and removes portions of the original machine vibration waveform data that do not indicate an occurrence of a vibration event related to a potential fault in the machine. The vibration data collection device then stores thinned waveform data that includes blocks of the original machine vibration waveform data and excludes the portions that have been removed. A data analysis computer generates a thinned waveform plot based on the thinned waveform data. In some embodiments, the thinned waveform plot includes the blocks of original machine vibration waveform data separated in time by gaps representing the portions that have been removed. In some embodiments, the thinned waveform plot that includes the blocks of the original machine vibration waveform data and representative blocks of data that each represent the portions of the original machine vibration waveform data that have been removed. A display device displays the thinned waveform plot for viewing by an analyst.

Graphical display of discontinuous waveform data

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 computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (F) and the number of lines of resolution (N) to be used in collecting machine vibration data so as to adequately distinguish between spectral peaks for identifying faults in machine bearings. The process can be extended to any other types of fault frequencies that a machine may exhibit, such as motor fault frequencies, pump/fan fault frequencies, and gear mesh fault frequencies. Embodiments of the process also ensure that the time needed to acquire the waveform is optimized. This is particularly useful when collecting data using portable vibration monitoring devices.

Waveform acquisition optimization

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 vibration measurement and analysis system identifies faulty bearings in a machine based on spectral vibration data. The system includes vibration sensors attached to the machine that generate vibration signals. A vibration data collector generates vibration spectral data based on the vibration signals. The vibration spectral data comprises vibration amplitude versus frequency data that includes peak amplitudes at corresponding peak frequencies. At least some of the peak amplitudes are associated with vibration generated by the faulty bearings. A vibration analysis computer processes the vibration spectral data to (1) locate the largest peak amplitudes, (2) search a bearing fault frequency library to generate a list of identified bearings having bearing fault frequencies matching the peak frequencies of the largest peak amplitudes, (3) determine a normalized accuracy error for each of the identified bearings, and (4) select from the list one of the identified bearings having a smallest normalized accuracy error.

Bearing and fault frequency identification from vibration spectral plots

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

A method for filling a gap of missing vibration data in a set of vibration data. At least one reference waveform on a first side of the gap is selected and at least one adjacent waveform on an opposing second side of the gap is selected. It is determined whether the gap is in a section of the vibration data where a frequency of the vibration data is one of increasing, decreasing, and steady state. Where the gap is in a section of the vibration data where the frequency of the vibration data is changing substantially linearly, an analytical method is applied to at least one of the at least one reference waveform and the at least one adjacent waveform to approximate the vibration data that is missing in the gap. Where the gap is in a section of the vibration data where the frequency of the vibration data is changing substantially exponentially, a numerical method is applied to at least one of the at least one reference waveform and the at least one adjacent waveform to approximate the vibration data that is missing in the gap. Where the gap is in a section of the vibration data where the frequency of the vibration data is substantially steady state, at least one of the at least one reference waveform and the at least one adjacent waveform is copied to approximate the vibration data that is missing in the gap. The approximated vibration data is presented to a user.

Waveform gap filling

A computerized apparatus for presenting vibration data that is read under different conditions as a single trend line on a chart. A sensor measures vibration and produces vibration data. A memory stores the vibration data, an indicator of the condition under which the vibration data was produced, and a type associated with the vibration data. A processor reads the vibration data, the condition, and the type from the memory, and selectively plots the vibration data on the trend line when the type of the vibration data matches a given value, even though the condition might be different from data point to data point on the trend line. The processor selectively creates a flag indicating a condition change, and an interface presents a plot of the trend line, and the flag when a condition change occurs between data points.

Trend plot with multiple acquisitions

A method for producing a thinned representation of vibration waveform data. The waveform vibration data is received and divided into sequential blocks. For each sequential block, each serially designated in turn as a current block, the following steps are performed. When the current block is also a first block, the current block is passed as a reference block. A representative value for the current block is computed and compared to the representative value for the reference block to determine a difference. The representative value for the current block is compared to a minimum representative value. The current block is transformed into a spectrum and compared to the spectrum for the reference block to determine a correlation value. When the representative value for the current block is above the minimum representative value, the current block is passed as the reference block whenever at least one of the following is true, (a) the first difference is greater than a given difference, (b) the correlation value is less than a given correlation value, and (c) a numerical count of blocks between the current block and a most recently passed reference block is greater than a given maximum.

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Knoxville, TN, United States of America

Anthony John Hayzen