Apparatus and method for identifying health indicators for rolling element bearings

• US 9,618,037 B2
• Filed: 07/15/2009
• Issued: 04/11/2017
• Est. Priority Date: 08/01/2008
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Kini, Venkataramana B., Kar, Chinmaya, Sadana, Rajat, Sunderam, Meenakshi, Mukherjee, Joydeb, Agrawal, Vedika
• Primary Examiner(s): Henson, Mischita
• Assistant Examiner(s): LIAO, CHRISTINE Y
• Application Number:
  US12/503,783
• Publication Number:
  US 20100030492A1
• Time in Patent Office: 2,827 Days
• Field of Search: 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, by a bearing health monitoring device, an input signal from a vibration sensor, 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;

decomposing the input signal into a frequency-domain signal;

determining multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing, each family of frequencies comprising a defect frequency associated with a particular failure mode and a number of harmonics of the defect frequency;

generating a first reconstructed input signal using the multiple families of frequencies and the frequency-domain signal;

generating an additional reconstructed input signal for each failure mode using the family of frequencies associated with that failure mode, wherein generating the additional reconstructed input signal for each failure mode comprises identifying maximum amplitudes of the frequency-domain signal in multiple frequency windows, each frequency window comprising a different frequency range associated with one of the frequencies in the family of frequencies for that failure mode;

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;

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

outputting the indicator to allow a determination regarding a repair or replacement 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 first 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 first 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 multiple statistical values including a normalized root mean square (RMS) value and a normalized Kurtosis value.

6. The method of claim 5, wherein determining whether the one or more normalized statistical values exceed the 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 generating the first reconstructed input signal comprises:

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

8. The method of claim 1, wherein the multiple failure modes of the rolling element bearing comprise a failure mode associated with each of an inner race, an outer race, a ball, and a cage of the rolling element bearing.

9. The method of claim 1, wherein the multiple frequency windows comprise at least three frequency windows having a same frequency as a midpoint of the frequency range of each frequency window.

10. An apparatus comprising:

at least one interface configured to receive from a vibration sensor 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 multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing, each family of frequencies comprising a defect frequency associated with a particular failure mode and a number of harmonics of the defect frequency;

generate a first reconstructed input signal using the multiple families of frequencies and the frequency-domain signal;

generate an additional reconstructed input signal for each failure mode using the family of frequencies associated with that failure mode by identifying maximum amplitudes of the frequency-domain signal in multiple frequency windows, each frequency window comprising a different frequency range associated with one of the frequencies in the family of frequencies for that failure mode;

use 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;

determine, using the first reconstructed input signal, an indicator identifying an overall health of the rolling element bearing; and

output the indicator to allow a determination regarding a repair or replacement of the rolling element bearing.

11. The apparatus of claim 10, wherein the at least one processing device is configured to generate the first reconstructed input signal by forming a union using the maximum amplitudes and the frequencies within the multiple families of frequencies.

12. The apparatus of claim 10, wherein the at least one processing device is configured to determine the indicator identifying the overall health of the rolling element bearing by:

generating one or more statistical values using the first 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.

13. The apparatus of claim 12, 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.

14. The apparatus of claim 13, 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.

15. The apparatus of claim 10, wherein the multiple failure modes of the rolling element bearing comprise a failure mode associated with each of an inner race, an outer race, a ball, and a cage of the rolling element bearing.

16. The apparatus of claim 10, wherein the multiple frequency windows comprise at least three frequency windows having a same frequency as a midpoint of the frequency range of each frequency window.

17. A non-transitory computer readable storage medium containing a computer program that causes a processing system to perforin a process, the computer program comprising:

computer readable program code configured to cause the processing system to decompose an input signal from a vibration sensor 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 configured to cause the processing system to determine multiple families of frequencies corresponding to multiple failure modes of the rolling element bearing, each family of frequencies comprising a defect frequency associated with a particular failure mode and a number of harmonics of the defect frequency;

computer readable program code configured to cause the processing system to generate a first reconstructed input signal using the multiple families of frequencies and the frequency-domain signal;

computer readable program code configured to cause the processing system to generate an additional reconstructed input signal for each failure mode using the family of frequencies associated with that failure mode by identifying maximum amplitudes of the frequency-domain signal in multiple frequency windows, each frequency window comprising a different frequency range associated with one of the frequencies in the family of frequencies for that failure mode;

computer readable program code configured to cause the processing system to use 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;

computer readable program code configured to cause the processing system to determine, using the first reconstructed input signal, an indicator identifying an overall health of the rolling element bearing; and

computer readable program code configured to cause the processing system to output the indicator to allow a determination regarding a repair or replacement of the rolling element bearing.

18. The non-transitory computer readable storage medium of claim 17, wherein the computer readable program code configured to cause the processing system to determine the indicator identifying the overall health of the rolling element bearing comprises:

computer readable program code configured to cause the processing system to generate statistical values using the first reconstructed input signal;

computer readable program code configured to cause the processing system to normalize 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 configured to cause the processing system to determine whether the normalized statistical values exceed specified thresholds.

19. The non-transitory computer readable storage medium of claim 18, wherein the computer readable program code configured to cause the processing system to determine whether the normalized statistical values exceed the specified thresholds comprises:

computer readable program code configured to cause the processing system to use 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.

20. The non-transitory computer readable storage medium of claim 18, wherein the multiple failure modes of the rolling element bearing comprise a failure mode associated with each of an inner race, an outer race, a ball, and a cage of the rolling element bearing.