APPARATUS AND METHOD FOR IDENTIFYING HEALTH INDICATORS FOR ROLLING ELEMENT BEARINGS

• US 20100030492A1
• Filed: 07/15/2009
• Published: 02/04/2010
• Est. Priority Date: 08/01/2008
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Sadana, Rajat, Agrawal, Vedika, Kini, Venkataramana B., Suderam, Meenakshi, Kar, Chinmaya, Mukherjee, Joydeb
• Granted Patent: US 9,618,037 B2
• Time in Patent Office: Days
• US Class Current: 702/39
• CPC Class Codes: F16C 19/52 with devices affected by abnormal or undesired conditions
• CPC Class Codes: F16C 2233/00 Monitoring condition, e.g. temperature, load, vibration
• CPC Class Codes: F16C 41/00 Other accessories, e.g. devices integrated in the bearing not relating to the bearing function as such
• CPC Class Codes: G01H 1/00 Measuring characteristics of vibrations in solids by using direct conduction to the detector G01H9/00, G01H11/00 take precedence
• CPC Class Codes: G01M 13/045 Acoustic or vibration analysis

Abstract

A method includes receiving an input signal containing information associated with a rolling element bearing and/or a piece of equipment containing the rolling element bearing. The method also includes decomposing the input signal into a frequency-domain signal and determining at least one family of frequencies corresponding to at least one failure mode of the rolling element bearing. The method further includes generating a reconstructed input signal using the at least one family of frequencies and the frequency-domain signal. In addition, the method includes determining, using the reconstructed input signal, an indicator identifying an overall health of the rolling element bearing. The indicator could be determined using a baseline signal associated with either (i) normal operation of the rolling element bearing and/or the piece of equipment or (ii) defective operation of the rolling element bearing and/or the piece of equipment (where a severity of the defective operation will increase over time).

Claims

1. A method comprising:

receiving an input signal comprising information associated with at least one of;

a rolling element bearing and a piece of equipment containing the rolling element bearing;

decomposing the input signal into a frequency-domain signal;

determining at least one family of frequencies corresponding to at least one failure mode of the rolling element bearing;

generating a reconstructed input signal using the at least one family of frequencies and the frequency-domain signal; and

determining, using the reconstructed input signal, an indicator identifying an overall health of the rolling element bearing.

2. The method of claim 1, wherein determining the indicator identifying the overall health of the rolling element bearing comprises:

generating one or more first statistical values or severity index values using the reconstructed input signal;

generating one or more second statistical values or severity index values using a baseline signal, the baseline signal associated with normal operation of at least one of;

the rolling element bearing and the piece of equipment; and

determining the indicator using the first and second statistical values or severity index values.

3. The method of claim 2, wherein determining the indicator using the first and second statistical values or severity index values comprises:

determining a difference between the first and second statistical values or severity index values; and

determining whether the difference exceeds at least one threshold.

4. The method of claim 1, wherein determining the indicator identifying the overall health of the rolling element bearing comprises:

generating one or more statistical values using the reconstructed input signal;

normalizing the one or more statistical values using a baseline signal, the baseline signal associated with one of;

normal operation of at least one of;

the rolling element bearing and the piece of equipment; and

defective operation of at least one of;

the rolling element bearing and the piece of equipment, where a severity of the defective operation will increase over time; and

determining whether the one or more normalized statistical values exceed one or more thresholds.

5. The method of claim 4, wherein the one or more normalized statistical values comprise a normalized RMS value and a normalized Kurtosis value.

6. The method of claim 5, wherein determining whether the one or more normalized statistical values exceed one or more thresholds comprises:

using one or more Fuzzy Logic input membership functions and one or more Fuzzy Logic output membership functions to process the normalized RMS value and the normalized Kurtosis value.

7. The method of claim 1, wherein:

determining the at least one family of frequencies comprises determining multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing; and

further comprising;

generating an additional reconstructed input signal for each failure mode using the family of frequencies associated with that failure mode; and

using the additional reconstructed input signal associated with each failure mode to determine a value identifying a likelihood that the rolling element bearing is suffering from a defect associated with that failure mode.

8. The method of claim 7, wherein generating the additional reconstructed input signal for one of the failure modes comprises:

identifying maximum amplitudes of the frequency-domain signal in multiple windows, each window associated with one of the frequencies in the family of frequencies for that failure mode.

9. The method of claim 8, wherein generating the reconstructed input signal comprises:

forming a union using the maximum amplitudes and the frequencies within the multiple families of frequencies.

10. An apparatus comprising:

at least one interface configured to receive an input signal comprising information associated with at least one of;

a rolling element bearing and a piece of equipment containing the rolling element bearing; and

at least one processing device configured to;

decompose the input signal into a frequency-domain signal;

determine a family of frequencies corresponding to a failure mode of the rolling element bearing;

generate a reconstructed input signal using the family of frequencies and the frequency-domain signal; and

determine, using the reconstructed input signal, a value identifying a likelihood that the rolling element bearing is suffering from a defect associated with the failure mode.

11. The apparatus of claim 10, wherein the at least one processing device is further configured to:

determine multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing;

generate multiple reconstructed input signals, each reconstructed input signal generated using one of the families of frequencies; and

determine multiple values identifying likelihoods that the rolling element bearing is suffering from defects associated with the failure modes.

12. The apparatus of claim 11, wherein the at least one processing device is further configured to:

generate an additional reconstructed input signal using the multiple families of frequencies; and

determine, using the additional reconstructed input signal, an indicator identifying an overall health of the rolling element bearing.

13. The apparatus of claim 12, wherein:

the at least one processing device is configured to generate each reconstructed input signal by identifying maximum amplitudes of the frequency-domain signal in multiple windows, each window associated with one of the frequencies in the family of frequencies for one of the failure modes; and

the at least one processing device is configured to generate the additional reconstructed input signal by forming a union using the maximum amplitudes and the frequencies within the multiple families of frequencies.

14. The apparatus of claim 10, wherein the at least one processing device is configured to determine the value identifying the likelihood that the rolling element bearing is suffering from the defect by:

generating one or more statistical values using the reconstructed input signal;

normalizing the one or more statistical values using a baseline signal, the baseline signal associated with one of;

normal operation of at least one of;

the rolling element bearing and the piece of equipment; and

defective operation of at least one of;

the rolling element bearing and the piece of equipment, where a severity of the defective operation will increase over time; and

determining whether the one or more normalized statistical values exceed one or more thresholds.

15. The apparatus of claim 14, wherein the at least one processing device is configured to determine whether the one or more normalized statistical values exceed the one or more thresholds using one or more Fuzzy Logic input membership functions and one or more Fuzzy Logic output membership functions.

16. The apparatus of claim 15, further comprising:

a display configured to present a graphical user interface to a user, the graphical user interface configured to receive information from the user defining the membership functions.

17. A computer readable medium embodying a computer program, the computer program comprising:

computer readable program code for decomposing an input signal to produce a frequency-domain signal, the input signal comprising information associated with at least one of;

a rolling element bearing and a piece of equipment containing the rolling element bearing;

computer readable program code for determining multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing;

computer readable program code for generating multiple reconstructed input signals, each reconstructed input signal generated using at least one family of frequencies and the frequency-domain signal; and

computer readable program code for determining, using the reconstructed input signals, (i) an indicator identifying an overall health of the rolling element bearing and (ii) values identifying likelihoods that the rolling element bearing is suffering from defects associated with the failure modes.

18. The computer readable medium of claim 17, wherein the computer readable program code for generating the multiple reconstructed input signals comprises:

computer readable program code for generating first reconstructed input signals, each first reconstructed input signal associated with one of the failure modes and generated using maximum amplitudes of the frequency-domain signal in multiple windows, each window associated with one of the frequencies in the family of frequencies for that failure mode; and

computer readable program code for generating a second reconstructed input signal, the second reconstructed input signal generated using the maximum amplitudes and the frequencies within the multiple families of frequencies.

19. The computer readable medium of claim 17, wherein the computer readable program code for determining the indicator identifying the overall health of the rolling element bearing and the values identifying the likelihoods that the rolling element bearing is suffering from the defects comprises:

computer readable program code for generating statistical values using the reconstructed input signals;

computer readable program code for normalizing the statistical values using a baseline signal, the baseline signal associated with one of;

normal operation of at least one of;

the rolling element bearing and the piece of equipment; and

defective operation of at least one of;

the rolling element bearing and the piece of equipment, where a severity of the defective operation will increase over time; and

computer readable program code for determining whether the normalized statistical values exceed specified thresholds.

20. The computer readable medium of claim 19, wherein the computer readable program code for determining whether the normalized statistical values exceed specified thresholds comprises:

computer readable program code for using one or more Fuzzy Logic input membership functions and one or more Fuzzy Logic output membership functions to determine the indicator identifying the overall health of the rolling element bearing.