Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A “store on alert” vibration data acquisition mechanism uses scalar data produced by a vibration monitoring device as a predicate to capturing and storing analytical vibration data in the vibration monitoring device. The scalar data may consist of scalar process variables generated in the vibration monitoring device that are acquired at a fixed interval, such as PeakVue and Overall Vibration. At each interval, these scalar data values are compared to machine performance threshold levels, such as ADVISE, MAINT and FAIL, to determine whether analytical vibration data is to be stored separately inside the vibration monitoring device. Since the analytical vibration data is captured based on a predicate inside the vibration monitoring device (i.e., comparison of the scalar value to the thresholds), the analytical vibration data includes more relevant diagnostic information about a specific machine performance event.

Abstract: 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.

Abstract: 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.

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 “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

Abstract: A method is described for automatically determining a proper threshold for a tachometer signal in order to produce desired tachometer pulses necessary for analysis of machine vibration data. A tachometer signal is low-pass filtered to exclude high frequency noise and a running derivative of the filtered tachometer waveform is taken to create a derivative waveform. Another waveform is created that includes only positive values from the derivative waveform that correspond to positive values in the low-pass filtered tachometer waveform. In general, a tachometer signal has the greatest derivative value (slope) when a tachometer pulse is present. Based on this observation, a threshold value is determined using both the low-pass filtered tachometer waveform and the positive-value derivative waveform along with statistics from both waveforms.

Abstract: A method is described for automatically determining a proper threshold for a tachometer signal in order to produce desired tachometer pulses necessary for analysis of machine vibration data. A tachometer signal is low-pass filtered to exclude high frequency noise and a running derivative of the filtered tachometer waveform is taken to create a derivative waveform. Another waveform is created that includes only positive values from the derivative waveform that correspond to positive values in the low-pass filtered tachometer waveform. In general, a tachometer signal has the greatest derivative value (slope) when a tachometer pulse is present. Based on this observation, a threshold value is determined using both the low-pass filtered tachometer waveform and the positive-value derivative waveform along with statistics from both waveforms.

Abstract: A laser strobe tachometer combines a strobe light with a laser speed sensor. The ability of the laser sensor to accurately determine machine speed enables a synchronous strobe rate to be determined without user intervention. Having locked onto the turning speed, the stroboscope can “freeze” the motion of the shaft, thereby allowing an operator to observe locations, such as on a keyway, flat or screw head, for the optimum placement of the laser spot to generate a once-per-revolution tachometer pulse.

Abstract: A laser strobe tachometer combines a strobe light with a laser speed sensor. The ability of the laser sensor to accurately determine machine speed enables a synchronous strobe rate to be determined without user intervention. Having locked onto the turning speed, the stroboscope can “freeze” the motion of the shaft, thereby allowing an operator to observe locations, such as on a keyway, flat or screw head, for the optimum placement of the laser spot to generate a once-per-revolution tachometer pulse.

Abstract: 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.

Abstract: A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

Abstract: A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

Abstract: Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.

Abstract: A “store on alert” vibration data acquisition mechanism uses scalar data produced by a vibration monitoring device as a predicate to capturing and storing analytical vibration data in the vibration monitoring device. The scalar data may consist of scalar process variables generated in the vibration monitoring device that are acquired at a fixed interval, such as PeakVue and Overall Vibration. At each interval, these scalar data values are compared to machine performance threshold levels, such as ADVISE, MAINT and FAIL, to determine whether analytical vibration data is to be stored separately inside the vibration monitoring device. Since the analytical vibration data is captured based on a predicate inside the vibration monitoring device (i.e., comparison of the scalar value to the thresholds), the analytical vibration data includes more relevant diagnostic information about a specific machine performance event.

Abstract: A method is described for automatically determining a proper threshold for a tachometer signal in order to produce desired tachometer pulses necessary for analysis of machine vibration data. A tachometer signal is low-pass filtered to exclude high frequency noise and a running derivative of the filtered tachometer waveform is taken to create a derivative waveform. Another waveform is created that includes only positive values from the derivative waveform that correspond to positive values in the low-pass filtered tachometer waveform. In general, a tachometer signal has the greatest derivative value (slope) when a tachometer pulse is present. Based on this observation, a threshold value is determined using both the low-pass filtered tachometer waveform and the positive-value derivative waveform along with statistics from both waveforms.

Abstract: A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

Abstract: A method is described for automatically determining a proper threshold for a tachometer signal in order to produce desired tachometer pulses necessary for analysis of machine vibration data. A tachometer signal is low-pass filtered to exclude high frequency noise and a running derivative of the filtered tachometer waveform is taken to create a derivative waveform. Another waveform is created that includes only positive values from the derivative waveform that correspond to positive values in the low-pass filtered tachometer waveform. In general, a tachometer signal has the greatest derivative value (slope) when a tachometer pulse is present. Based on this observation, a threshold value is determined using both the low-pass filtered tachometer waveform and the positive-value derivative waveform along with statistics from both waveforms.