Selective decimation and analysis of oversampled data
First Claim
1. A method for processing dynamic measurement data derived from an analog signal generated by an analog sensor in sensory contact with a machine or a process, and displaying processed dynamic measurement data on a display device, the method comprising:
- (a) generating the analog signal by the analog sensor in sensory contact with the machine or process;
(b) converting the analog signal into an oversampled digital data stream;
(c) designating sampling interval datasets within the oversampled digital data stream;
(d) displaying a measurement selector on the display device to allow a user to select a measurement to be used during a decimation process, wherein the measurement is selected from one or more of a median value, a mode value, a standard deviation value (SDV), a maximum value, a range value, a minimum value, a root mean square (RMS) value, a statistical scatter value, a momentum value, a variance value, a skewness value, a kurtosis value, a peak shape factor (PSF) characteristic, a parametric-versus-causal (PvC) characteristic, and one or more difference values;
(e) receiving a measurement selection based on operation of the measurement selector by the user;
(f) decimating sequential sampling interval datasets based on the measurement selection to produce scalar values corresponding to each sampling interval dataset;
(g) generating a waveform comprising the scalar values; and
(h) displaying the waveform on the display device.
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Accused Products
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.
24 Citations
34 Claims
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1. A method for processing dynamic measurement data derived from an analog signal generated by an analog sensor in sensory contact with a machine or a process, and displaying processed dynamic measurement data on a display device, the method comprising:
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(a) generating the analog signal by the analog sensor in sensory contact with the machine or process; (b) converting the analog signal into an oversampled digital data stream; (c) designating sampling interval datasets within the oversampled digital data stream; (d) displaying a measurement selector on the display device to allow a user to select a measurement to be used during a decimation process, wherein the measurement is selected from one or more of a median value, a mode value, a standard deviation value (SDV), a maximum value, a range value, a minimum value, a root mean square (RMS) value, a statistical scatter value, a momentum value, a variance value, a skewness value, a kurtosis value, a peak shape factor (PSF) characteristic, a parametric-versus-causal (PvC) characteristic, and one or more difference values; (e) receiving a measurement selection based on operation of the measurement selector by the user; (f) decimating sequential sampling interval datasets based on the measurement selection to produce scalar values corresponding to each sampling interval dataset; (g) generating a waveform comprising the scalar values; and (h) displaying the waveform on the display device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
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31. A method for collecting and processing oversampled vibration data collected by at least first and second vibration sensors attached to a mechanical structure used in the processing of a material, wherein the mechanical structure is operable to transmit vibrational energy from the material to the first and second vibration sensors, wherein the oversampled vibration data comprises a plurality of sampling interval datasets, wherein each sampling interval dataset corresponds to a particular sampling interval, the method comprising:
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(a) positioning the first vibration sensor in a first location on the mechanical structure and positioning the second vibration sensor in a second location on the mechanical structure; (b) receiving vibrational energy at the first vibration sensor, wherein the vibrational energy was generated by an event that occurs within the material being processed and traveled through the mechanical structure to the first vibration sensor; (c) the first vibration sensor generating a first vibration signal based on the vibrational energy; (d) oversampling the first vibration signal to generate first oversampled vibration data comprising a plurality of first sampling interval datasets; (e) for each of the plurality of first sampling interval datasets, determining one or more first scalar values selected from the group consisting of a maximum value, a minimum value, a mean value, a median value, a mode value, a standard deviation value, a maximum-to-minimum range value, a kurtosis value, a skewness value, and a wavelength value; (f) based on the one or more first scalar values, determining one or more first characteristic values that provide an indication of an event type; (g) generating a first timestamp value representative of a time at which the vibrational energy generated by the event was received at the first vibration sensor; (h) receiving the vibrational energy at the second vibration sensor, wherein the vibrational energy traveled through the mechanical structure to the second vibration sensor; (i) the second vibration sensor generating a second vibration signal based on the vibrational energy; (j) oversampling the second vibration signal to generate second oversampled vibration data comprising a plurality of second sampling interval datasets; (k) for each of the plurality of second sampling interval datasets, determining one or more second scalar values selected from the group consisting of a maximum value, a minimum value, a mean value, a median value, a standard deviation value, a maximum-to-minimum range value, a kurtosis value, a skewness value, and a wavelength value; (l) based on the one or more second scalar values, determining one or more second characteristic values that provide an indication of the event type; (m) generating a second timestamp value representative of a time at which the vibrational energy generated by the event was received at the second vibration sensor; (n) comparing the one or more first characteristic values to the one or more second characteristic values to determine that the event type indicated by the one or more first characteristic values is the same event type as indicated by the one or more second characteristic values; (o) determining a time difference between the first timestamp and the second timestamp; and (p) based at least in part on the time difference, determining a location of the event relative to the first vibration sensor and the second vibration sensor. - View Dependent Claims (32, 33)
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34. An apparatus for collecting and processing machine or process vibration data comprising:
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at least one vibration sensor attached to a machine, the at least one vibration sensor generating at least one analog vibration signal having a maximum frequency of interest, FMAX, which is greater than an event frequency of events occurring in the machine or the process; at least one analog-to-digital converter for oversampling the at least one analog vibration signal at a sampling rate of at least seven times FMAX to generate a plurality of sampling interval datasets, each corresponding to a particular sampling interval; a decimation module having a plurality of parallel field programmable gate arrays comprising; a first field programmable gate array for receiving the plurality of sampling interval datasets and determining a first scalar value from each sampling interval dataset, the first scalar selected from the group consisting of a maximum value, a minimum value, a median value, a mode value, a mean value, a standard deviation value, a parametric-versus-causal value, an operational condition value, and a peak shape factor value; and a second field programmable gate array for receiving the plurality of sampling interval datasets and determining a second scalar value from each sampling interval dataset that is different from the first scalar value, the second scalar selected from the group consisting of a maximum value, a minimum value, a median value, a mode value, a mean value, a standard deviation value, a parametric-versus-causal value, an operational condition value, and a peak shape factor value.
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Specification