Analysis of Periodic Information in a Signal
First Claim
1. A method for analyzing periodic information in a signal associated with a machine or process, the method comprising:
- (a) acquiring the signal over a time period using a sensor associated with the machine or process;
(b) generating an autocorrelation waveform based on the signal;
(c) determining a periodic signal parameter value based at least in part on the autocorrelation waveform, the periodic signal parameter value comprising a single real number indicative of a level of periodic information in the signal.
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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.
10 Citations
15 Claims
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1. A method for analyzing periodic information in a signal associated with a machine or process, the method comprising:
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(a) acquiring the signal over a time period using a sensor associated with the machine or process; (b) generating an autocorrelation waveform based on the signal; (c) determining a periodic signal parameter value based at least in part on the autocorrelation waveform, the periodic signal parameter value comprising a single real number indicative of a level of periodic information in the signal. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for analyzing periodic information in a signal associated with a machine or process, the method comprising:
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(a) acquiring the signal over a time period using a sensor associated with the machine or process; (b) performing a Fast Fourier Transform on the signal to generate a signal spectrum having amplitude values YVS(n), where n=1 to N, and N is a number of frequency values; (c) generating an autocorrelation waveform based on the signal; (d) performing a Fast Fourier Transform on the autocorrelation waveform to generate an autocorrelation spectrum having amplitude values YAS(n), where n=1 to N, where N is the number frequency values; (e) combining adjacent pairs of amplitude values YVS(2n) and YVS(2n−
1) in the signal spectrum, according to
YMCVS(n)=√
{square root over ((YVS(2n−
1))2+(YVS(2n))2)}{square root over ((YVS(2n−
1))2+(YVS(2n))2)},(f) combining the signal spectrum and the autocorrelation spectrum to generate a periodic information plot having amplitude values YPIP1(n), according to
YPIP1(n)=YMCVS(n)×
YAS(n),where n=1 to N. - View Dependent Claims (11, 12)
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13. A method for analyzing periodic information in a signal associated with a machine or process, the method comprising:
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(a) acquiring the signal over a time period using a sensor associated with the machine or process; (b) performing a Fast Fourier Transform on the signal to generate a signal spectrum having amplitude values YVS(n), where n=1 to M, and M is a number of frequency values; (c) generating an autocorrelation waveform based on the signal; (d) performing a Fast Fourier Transform on the autocorrelation waveform to generate an autocorrelation spectrum having amplitude values YAS(n), where n=1 to N, where N is the number of frequency values; (e) combining adjacent pairs of amplitude values YVS(2n) and YVS(2n−
1) in the signal spectrum, according to
YMCVS(n)=√
{square root over ((YVS(2n−
1))2+(YVS(2n))2)}{square root over ((YVS(2n−
1))2+(YVS(2n))2)}, and(f) generating a periodic information plot having amplitude values YPIP2(n), according to
If YAS(n)>
YTHR,YPIP2(n)=YMCVS(n)
If YAS(n)≦
YTHR,YPIP2(n)=0,where n=1 to N, and YTHR is a predetermined threshold value.
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14. A method for analyzing periodic information in a signal associated with a machine or process, the method comprising:
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(a) acquiring the signal over a time period using a sensor associated with the machine or process; (b) performing a Fast Fourier Transform on the signal to generate a signal spectrum having amplitude values YVS(n), where n=1 to N, where N is a number of frequency values; (c) combining adjacent pairs of amplitude values YVS(2n) and YVS(2n−
1) in the signal spectrum, according to
YMCVS(n)=√
{square root over ((YVS(2n−
1))2+(YVS(2n))2)}{square root over ((YVS(2n−
1))2+(YVS(2n))2)},(d) generating an autocorrelation waveform based on the signal; (e) performing a Fast Fourier Transform on the autocorrelation waveform to generate an autocorrelation spectrum having amplitude values YAS(n), where n=1 to N, where N is the number of frequency values; and (f) combining the signal spectrum and the autocorrelation spectrum to generate a periodicity map having coordinate values XPM(n) and YPM(n) determined according to
XPM(n)=YMCVS(n)
YPM(n)=YAS(n)for n=1 to N.
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15. A method for analyzing non-periodic information in a signal associated with a machine or process, the method comprising:
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(a) acquiring the signal over a time period using a sensor associated with the machine or process; (b) generating an autocorrelation waveform based on the signal; (c) performing a Fast Fourier Transform on the autocorrelation waveform to generate an autocorrelation spectrum having amplitude values YAS(n), where n=1 to N; and (d) generating a non-periodic information plot having amplitude values YNPIP(n), according to
If YAS(n)<
YTHR,YNPIP(n)=YAS(n)
If YAS(n)≧
YTHR,YNPIP(n)=0,where n=1 to N, and YTHR is a predetermined threshold value.
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Specification