Coherent phase line enhancer spectral analysis technique

• US 6,408,696 B1
• Filed: 04/05/2000
• Issued: 06/25/2002
• Est. Priority Date: 12/06/1999
• Status: Expired due to Term

• Current Assignee: Al Signal Research Inc.
• Original Assignee: AI Signal Research, Inc.
• Inventors: Jong, Jen-Yi
• Primary Examiner(s): MOLLER, RICHARD ALAN
• Application Number:
  US09/543,657
• Time in Patent Office: 811 Days
• Field of Search: 735/79, 735/70, 735/84, 735/93, 736/49, 736/59, 736/60, 702/56, 702/57, 702/66, 702/75, 702/76, 702/77, 324/76.19, 324/76.21, 340/680, 340/683
• US Class Current: 73/579
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01R 23/16 Spectrum analysis; Fourier analysis

Abstract

A computer system and method for detecting periodic and quasi-periodic signals buried in wide-band noise is disclosed. For a signal containing periodic signals, the method includes the steps of segmenting the signal into an ensemble of block signals and performing a first Discrete Fourier Transform (DFT) on each block signal to obtain an ensemble of frequency signals containing complex data and frequency components. Next, the method includes the steps of defining an ensemble of complex frequency-time signals using the complex data from each frequency signal, performing a second DFT on each complex frequency-time signal to obtain an equivalent wave number signal for each frequency component, and identifying a peak magnitude component for each equivalent wave number signal. Finally, the method includes the steps of defining a window around each peak magnitude component associated with each frequency component, calculating a widow power for each window, and displaying the window power for each frequency component to form a CPLE power spectral density plot. For a signal containing a quasi-periodic signal, the method includes an initial step of transforming the quasi-periodic signal into a periodic signal. The method enhances detection by increasing the signal to noise ratio of the periodic and quasi-periodic signals. In addition, the method provides an estimate of the frequency associated with, as well as, a CPLE coherence value for signals. The method is implemented using a software program and a conventional computer system.

Claims

1. A method for detecting a periodic signal buried in wide-band noise in a signal using a CPLE power spectral density plot, comprising the steps of:

(a) segmenting the signal into a plurality of adjacent, equal-size, block signals;

(b) performing a first Discrete Fourier Transform on each block signal to obtain an ensemble of frequency signals, each frequency signal including complex data associated with frequency components;

(c) defining a complex frequency-time signal for each frequency component using the complex data from each frequency signal;

(d) performing a second Discrete Fourier Transform on each complex frequency-time signal to obtain an equivalent wave number signal for each frequency component;

(e) identifying a peak magnitude component for each equivalent wave number signal associated with each frequency component;

(f) defining a window around each peak magnitude component for each frequency component;

(g) calculating a window power for each frequency component;

(h) displaying the window power for each frequency component to obtain the CPLE power spectral density plot of the signal, wherein a peak component in the CPLE power spectral density plot represents the periodic signal.

2. The method of claim 1, wherein the step of performing a first Discrete Fourier Transform comprises performing a first Fast Fourier Transform and the step of performing a second Discrete Fourier Transform comprises performing a second Fast Fourier Transform.

3. The method of claim 1, further comprising the steps of:

(a) identifying a peak wave number component associated with the peak magnitude component in each equivalent wave number signal;

(b) identifying a peak frequency component associated with the peak magnitude component in each equivalent wave number signal;

(c) multiplying the peak wave number component by a predetermined constant to obtain a frequency correction value;

(d) summing the frequency correction value and the peak frequency component to obtain an estimate of an actual frequency associated with the periodic signal.

4. The method of claim 1, further comprising the steps of:

(a) dividing the power associated with each frequency component from the CPLE PSD by a power associated with each frequency component from a conventional PSD to obtain a CPLE coherence value for each frequency component; and

(b) displaying the CPLE coherence value for each frequency component.

5. The method of claim 1, wherein the signal contains a quasi-periodic signal and the method further comprises an initial step of transforming the quasi-periodic signal into a periodic signal using a predetermined transformation.

6. The method of claim 5, wherein the predetermined transformation is a PSEM transformation.

7. The method of claim 5, wherein the predetermined transformation is an OT transformation.

8. A method for detecting a mechanical defect in a rotary machine using a CPLE power spectral density plot, comprising the steps of:

(a) generating a vibration signal from the rotary machine, the vibration signal including a periodic signal representative of the mechanical defect in the rotary machine;

(b) segmenting the vibration signal into a plurality of adjacent, equal-size, block signals;

(c) performing a first Discrete Fourier Transform on each block signal to obtain an ensemble of frequency signals, each frequency signal including complex data associated with frequency components;

(d) defining a complex frequency-time signal for each frequency component using the complex data from each frequency signal;

(e) performing a second Discrete Fourier Transform on each complex frequency-time signal to obtain an equivalent wave number signal for each frequency component;

(f) identifying a peak magnitude component for each equivalent wave number signal associated with each frequency component;

(g) defining a window around each peak magnitude component for each frequency component;

(h) calculating a window power value for each frequency;

(i) displaying the window power for each frequency to obtain a CPLE power spectral density plot of the vibration signal, wherein a peak component in the CPLE power spectral density plot represents the mechanical defect in the rotary machine.

9. The method of claim 8, wherein the step of performing a first Discrete Fourier Transform comprises performing a first Fast Fourier Transform and the step of performing a second Discrete Fourier Transform comprises performing a second Fast Fourier Transform.

10. The method of claim 8, further comprising the steps of:

(a) identifying a peak wave number component associated with the peak magnitude component in each equivalent wave number signal;

(b) identifying a peak frequency component associated with the peak magnitude component in each equivalent wave number signal;

(c) multiplying the peak wave number component by a predetermined constant to obtain a frequency correction value;

(d) summing the frequency correction value and the peak frequency component to obtain an estimate of an actual frequency associated with the mechanical defect in the rotary machine.

11. The method of claim 8, further comprising the steps of:

(a) dividing the power associated with each frequency component from the CPLE PSD by a power associated with each frequency component from a conventional PSD to obtain a CPLE coherence value for each frequency component; and

(b) displaying the CPLE coherence value for each frequency component.

12. The method of claim 8, wherein the vibration signal includes a quasi-periodic signal and the method further comprises an initial step of transforming the quasi-periodic signal into a periodic signal using a predetermined transformation.

13. The method of claim 12, wherein the predetermined transformation is a PSEM transformation.

14. The method of claim 12, wherein the predetermined transformation is an OT transformation.

15. A system for identifying a signal containing a periodic signal that is at/east partially masked by one or more noise signals, comprising:

(a) a data input device operable to receive a data file containing a recording of the signal;

(b) a processor in communication with the data input device such that the processor can process the data file;

(c) computer software operatively associated with the processor and containing instructions that cause the processor to;

segment the data file into a plurality of adjacent, equal-size, blocks;

perform a first Discrete Fourier Transform on each block to obtain an ensemble of frequency blocks, each frequency block including complex data associated with frequency components;

define a complex frequency-time signal for each frequency component using the complex data from each frequency block;

perform a second Discrete Fourier Transform on each complex frequency-time signal to obtain an equivalent wave number signal for each frequency component;

identify a peak magnitude component for each equivalent wave number signal associated with each frequency component;

define a window around each peak magnitude component for each frequency component;

calculate a window power for each frequency component; and

(d) a data output device in communication with the processor and operative to provide an indication to a user of the system of the window power for each frequency component in a CPLE power spectral density plot of the data file such that a peak component in the CPLE power spectral density plot represents the periodic signal.

16. The system of claim 15, wherein the first Discrete Fourier Transform comprises a first Fast Fourier Transform and the second Discrete Fourier Transform comprises a second Fast Fourier Transform.

17. The system of claim 15, wherein the computer software operatively associated with the processor further contains instructions that cause the processor to:

identify a peak wave number component associated with the peak magnitude component in each equivalent wave number signal;

identify a peak frequency component associated with the peak magnitude component in each equivalent wave number signal;

multiply the peak wave number component by a predetermined constant to obtain a frequency correction value; and

sum the frequency correction value and the peak frequency component to obtain an estimate of an actual frequency associated with the periodic signal.

18. The system of claim 15, wherein the computer software operatively associated with the processor further contains instructions that cause the processor to:

divide the power associated with each frequency component from the CPLE PSD by a power associated with each frequency component from a conventional PSD to obtain a CPLE coherence value for each frequency component; and

display the CPLE coherence value for each frequency component using the data output device.

19. The system of claim 15, wherein the signal includes a quasi-periodic signal and the computer software operatively associated with the processor further contains instructions that cause the processor to transform the quasi-periodic signal into a periodic signal using an OT transformation.

20. The system of claim 15, wherein the signal includes a quasi-periodic signal and the computer software operatively associated with the processor further contains instructions that cause the processor to transform the quasi-periodic signal into a periodic signal using a PSEM transformation.

21. A method for detecting and identifying a signal corresponding to a vibration generated by a rotating mechanical part, wherein the signal is at least partially obscured by noise signals, comprising the steps of:

(a) supplying the signal and the noise signals as a data file to a processor;

(b) processing the data file using the processor by;

segmenting the data file into a plurality of adjacent, equal-size, blocks;

performing a first Discrete Fourier Transform on each block to obtain an ensemble of frequency blocks, each frequency block including complex data associated with frequency components;

defining a complex frequency-time signal for each frequency component using the complex data from each frequency block;

performing a second Discrete Fourier Transform on each complex frequency-time signal to obtain an equivalent wave number signal for each frequency component;

identifying a peak magnitude component for each equivalent wave number signal associated with each frequency component;

defining a window around each peak magnitude component for each frequency component;

calculating a window power for each frequency component; and

(c) displaying the window power for each frequency component to obtain a CPLE power spectral density plot of the data file, wherein a peak component in the CPLE power spectral density plot represents the signal.

22. The method of claim 21, wherein the step of performing a first Discrete Fourier Transform comprises performing a first Fast Fourier Transform and the step of performing a second Discrete Fourier Transform comprises performing a second Fast Fourier Transform.

23. The method of claim 21, further comprising the steps of:

identifying a peak wave number component associated with the peak magnitude component in each equivalent wave number signal;

identifying a peak frequency component associated with the peak magnitude component in each equivalent wave number signal;

multiplying the peak wave number component by a predetermined constant to obtain a frequency correction value; and

summing the frequency correction value and the peak frequency component to obtain an estimate of an actual frequency associated with the periodic signal.

24. The method of claim 21, further comprising the steps of:

dividing the power associated with each frequency component from the CPLE PSD by a power associated with each frequency component from a conventional PSD to obtain a CPLE coherence value for each frequency component; and

displaying the CPLE coherence value for each frequency component.

25. The method of claim 21, further comprising the step of:

using a sensor placed proximate to the rotating mechanical part to sense and convert the vibration of the mechanical part into the signal and the noise signals; and

converting and formatting the signal and noise signals to form the data file.

26. The method of claim 21, wherein the signal includes a quasi-periodic signal and the method further includes the step of transforming the quasi-periodic signal into a periodic signal using a PSEM transformation.

27. The method of claim 21, wherein the signal comprises a quasi-periodic signal and the method further includes the step of transforming the quasi-periodic signal into a periodic signal using an OT transformation.