Automated fault diagnosis device and method

• US 20030209077A1
• Filed: 06/13/2003
• Published: 11/13/2003
• Est. Priority Date: 01/23/2002
• Status: Active Grant

• Current Assignee: Computational Systems Incorporated (Emerson Electric Company)
• Original Assignee: CSI Technology Incorporated (Emerson Electric Company)
• Inventors: Robinson, James C., Van Voorhis, J. Brent, Hillard, Jason E., Battenberg, Rexford A.
• Granted Patent: US 7,010,445 B2
• Time in Patent Office: Days
• US Class Current: 73/587
• CPC Class Codes: G01N 2291/02881 Temperature
• CPC Class Codes: G01P 3/36 Devices characterised by the use of optical means, e.g. using infrared, visible, or ultraviolet light G01P3/68 takes precedence; gyrometers using the Sagnac effect, i.e. rotation-induced shifts between counter-rotating electromagnetic beams G01C19/64

Abstract

A diagnostic apparatus and method for sensing a signal emanating from a machine and determining a periodic machine event based on the emanated signal. The apparatus includes analog to digital processing components for digitizing the sensed signal, producing a digitized signal. The apparatus also includes a transform algorithm, memory, an analysis algorithm, and a display. The transform algorithm transforms the digitized signal to generate power spectral density and autocorrelation data which is stored in the memory. The analysis algorithm includes criteria for analyzing the stored power spectral density and autocorrelation data to determine a period machine event based at least in part upon the autocorrelation data. The display displays the periodic machine event to a user. In the event of a suspected defect or fault, the device alerts the user. The user may then elect to proceed with repairs or undertake advanced analysis of the suspected fault as economy dictates.

Claims

1. A method of determining a periodic event of a machine system comprising:

(a) receiving signals emanating from the system,

(b) transforming the received signals to produce first transformed signals,

(c) transforming the first transformed signals to produce second transformed signals having at least real components, and

(d) processing the transformed signals using select criteria to determine the periodic system event.

2. The method of claim 1 wherein (b) and (c) further comprise:

(b1) using a Fourier Transform function to transform the received signals to produce the first transformed signals which represent power spectral density data, and

(c1) using a Fourier Transform function to transform the first transformed signals to produce the second transformed signals which represent autocorrelation data.

3. The method of claim 2 wherein (d) further comprises locating one or more peaks in the power spectral density data and/or autocorrelation data.

4. The method of claim 3 wherein (d) further comprises performing peak comparisons using at least one peak in the power spectral density data and/or autocorrelation data to determine a periodic system event.

5. The method of claim 4 wherein (d) further comprises applying select criteria to the comparisons wherein the select criteria is used to determine 1) whether a searched for peak in the autocorrelation data is a fundamental frequency peak, 2) whether the searched for peak is within a number of bins of an exact frequency required, and 3) whether there are a select number of harmonics of the fundamental frequency peak.

6. The method of claim 3 wherein (d) further comprises searching side bands of one or more peaks in the autocorrelation data based on a select peak frequency in the power spectral density data, and determining a periodic system event based at least in part on the searched side bands.

7. The method of claim 3 wherein (d) further comprises limiting a number of peaks in the autocorrelation data, comparing at least one peak in the limited autocorrelation data with at least one peak in the power spectral density data, and determining whether a periodic machine event exists based at least in part on a result of the comparison.

8. The method of claim 3 wherein (d) further comprises searching for a peak in the power spectral density data which represents a periodic machine event and verifying that the peak represents a periodic machine event based at least in part on the autocorrelation data.

9. The method of claim 1 wherein (d) further comprises:

(d1) examining real components of the second transformed signals with a peak location algorithm, and

(d2) selecting a number of real components of the second transformed signals when;

1) a peak is not located in one of a first set of frequency bins;

2) a peak has an amplitude greater than a number multiplied by a largest amplitude peak in a select bin; and

3) another peak is located at another frequency which is related to the peak frequency and also satisfies the criteria in

     1) and

     2).

10. The method of claim 1 wherein (d) further comprises:

(d1) storing peak magnitude data of the first transformed signals in a number of frequency bins,

(d2) determining a number of largest magnitude peaks from the stored peak magnitude data with a peak location algorithm,

(d3) locating a first peak within the number of largest magnitude peaks which corresponds to a periodic event of the system, and

(d4) determining whether the first peak satisfies select criteria.

11. The method of claim 10 further comprising:

(d5) determining whether the first peak is located in one of a first set of frequency bins,

(d6) determining whether an amplitude of the first peak is greater than a number multiplied by a largest amplitude peak located in another other frequency bin,

(d7) determining whether a second peak is located at about two times a frequency of the first peak which is located in a different set of frequency bins and where an amplitude of the second peak is greater than a number multiplied by the largest amplitude peak located in another frequency bin, and

(d8) using the first peak to determine the periodic system event when steps (d5)-(d7) are satisfied.

12. The method of claim 11 further comprising:

(d9) determining from the stored peak magnitude data whether an amplitude of a third peak having a frequency is greater than about 0.90 multiplied by the amplitude of the first peak,

(d10) determining if a fourth peak located at about two times the third peak frequency also satisfies step (d9), and

(d11) using the third peak to determine the periodic system event when steps (d9)-(d10) are satisfied.

13. A sound sensing device for sensing sound and for determining a periodic event of a machine based thereon, the device comprising:

a sensor for sensing a sound signal emanating from the machine, the sensor operable to convert the sensed sound signal into an electrical signal,

signal processing components for digitizing the electrical signal to produce a digitized signal and for processing the digitized signal, and

firmware including transform algorithms for successively transforming the digitized signal to produce first and second sets of transform data, the second set being a transform of the first set, and algorithms for analyzing the two sets of transform data to determine a periodic machine event based on select criteria applied to the transform data.

14. The sensing device of claim 13 wherein the firmware further comprises a Fourier Transform algorithm for producing power spectral density data by transforming the digitized signal.

15. The sensing device of claim 14 wherein the firmware further comprises algorithmic structure for successively applying the Fourier Transform algorithm to produce autocorrelation data and for conditioning the autocorrelation data to produce conditioned autocorrelation data.

16. The sensing device of claim 15 further comprising a peak fit algorithm for fitting a curve to the power spectral data and the conditioned autocorrelation data and a peak location algorithm for locating one or more peaks in the power spectral data and the conditioned autocorrelation data.

17. The sensing device of claim 16 further comprising algorithmic structure for searching side bands of one or more peaks in the conditioned autocorrelation data based at least in part on a select peak frequency of the power spectral density data, and for determining a periodic event based at least in part on the searched side bands.

18. The sensing device of claim 16 further comprising algorithmic structure for comparing at least one peak in the conditioned autocorrelation data with at least one peak in the power spectral density data, wherein search criteria applied to a result of the peak comparison determines whether a periodic machine event exists.

19. The sensing device of claim 18 wherein the search criteria further comprises instructions for determining whether a searched for peak in the conditioned autocorrelation data is a fundamental frequency peak, the searched for peak is within a-number of bins of an exact frequency required, and there are a select number of harmonics of the fundamental frequency peak.

20. The sensing device of claim 16 further comprising algorithmic structure for limiting a number of peaks in the conditioned autocorrelation data and for comparing at least one peak in the limited autocorrelation data with at least one peak in the power spectral density data, wherein a result of the comparison is used to determine whether a periodic machine event exists.

21. The sensing device of claim 16 further comprising algorithmic structure for searching for a peak in the power spectral density data which represents a periodic machine event and for verifying that the peak represents a periodic machine event based at least in part on the conditioned autocorrelation data.

22. A diagnostic apparatus including a sensor for sensing a signal emanating from a machine and determining a periodic machine event based on the emanated signal, the apparatus comprising:

analog to digital processing components for digitizing the sensed signal to produce a digitized signal,

a transform algorithm for transforming the digitized signal to generate power spectral density and autocorrelation data from the digitized signal,

memory for storing the power spectral density data and autocorrelation data, an analysis algorithm for analyzing the stored power spectral density and autocorrelation data, the analysis algorithm including criteria for automatically determining a period machine event based at least in part upon the autocorrelation data, and

a display for displaying the periodic machine event to a user.

23. The apparatus of claim 22 wherein the transform algorithm further comprises a Fourier Transform function for producing the power spectral data after a first application to the digitized signal and autocorrelation data after a second application to the power spectral data, the apparatus determines the periodic machine event based at least in part upon one or more peaks in the power spectral density data and/or autocorrelation data.