Abstract: A computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (Fmax) and the number of lines of resolution (Nlines) 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.

Abstract: 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.

Abstract: A computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (Fmax) and the number of lines of resolution (Nlines) 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (Fmax) and the number of lines of resolution (Nlines) 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A process for thinning scalar machine vibration data can reduce the amount of data storage required to store the data by several orders of magnitude, without losing any important machine diagnostic vibration information. The process assumes that because each scalar vibration measurement value has its own range of values, there is a unique delta change in value that does not significantly impact machine diagnostic information provided by the data. Some embodiments provide a method to automatically evaluate the delta change in value. The process can be used to thin data that have already been stored in a database, and also to thin the data in real-time during data collection. Data storage structures for storing the thinned scalar values and processes for displaying a trend plot to indicate where the scalar data have been thinned are also described.

Abstract: 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.