System and method for proactive motor wellness diagnosis based on potential cavitation faults

• US 7,231,319 B2
• Filed: 06/02/2004
• Issued: 06/12/2007
• Est. Priority Date: 06/18/2003
• Status: Active Grant

• Current Assignee: Eaton Intelligent Power Limited (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Dimino, Steven A., Krstic, Slobodan, Liu, Yanzhen, Nowak, Michael P.
• Primary Examiner(s): Barlow; John
• Assistant Examiner(s): VO, HIEN XUAN
• Application Number:
  US10/859,525
• Publication Number:
  US 20050021302A1
• Time in Patent Office: 1,105 Days
• Field of Search: 702/185, 702/182, 702/77, 702/33, 324/72, 700/2, 700/159, 700/174, 700/169, 361 23- 35
• US Class Current: 702/185
• CPC Class Codes: G01R 31/343 in operation
• CPC Class Codes: G05B 2219/37351 Detect vibration, ultrasound
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: G05B 23/0245 based on a qualitative model, e.g. rule based; if-then decisions
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: H02H 7/0822 Integrated protection, motor control centres

Abstract

The present invention is directed to system and method for determining motor wellness based on potential faults including cavitation faults. Specifically, a controller configured to detect indicia of motor faults. The controller includes a processor configured to determine motor parameters of a given motor, generate a set of baseline data for the given motor, and acquire current data from the given motor during operation. The processor is also caused to isolate sidebands within the current data and map the current data within the sidebands to one of a plurality of bins. The processor is caused to compare the current data within the sidebands to baseline data from the set of baseline data associated with the bin and determine a predictive fault index of the given motor prior to an actual fault occurrence.

Claims

1. A controller configured to detect indicia of motor faults and having a processor configured to:

determine motor parameters of a given motor;

generate a set of baseline data for the given motor;

acquire current data from the given motor during operation;

isolate sidebands within the current data;

map the current data within the sidebands to one of a plurality of bins;

compare the current data within the sidebands to baseline data from the set of baseline data associated with the bin; and

determine a predictive fault index of the given motor prior to an actual fault occurrence.

2. The controller of claim 1 wherein the processor is further configured to communicate the fault index as an indication of a probability of an actual fault occurrence.

3. The controller of claim 1 wherein the processor is further configured to dynamically notch filter the current data to remove a fundamental frequency of the given motor.

4. The controller of claim 3 wherein the processor is further configured to track the fundamental frequency of the given motor over an operational range for use with an inverter.

5. The controller of claim 1 wherein the processor is further configured to digitize and decimate at least one of the current data and the power data to acquire a desired resolution for comparison with the baseline data.

6. The controller of claim 1 wherein the processor is further configured to compute at least one fast Fourier Transform (FFT) of raw current feedback from the given motor to derive the current data.

7. The controller of claim 6 wherein the processor is further configured to average the current data to stabilize the current data.

8. The controller of claim 6 wherein the processor is further configured to compare the current data to the baseline data associated with the mapped bin and determine an impending motor fault if the current data deviates from the baseline data by greater than a threshold.

9. The controller of claim 8 wherein the threshold is one of a user-defined threshold, a dynamically generated threshold, and a lookup table threshold.

10. The controller of claim 6 wherein the processor is further configured to isolate the sidebands within the current data based on the system frequency of the given motor in operation.

11. The controller of claim 10 wherein the processor is further configured to determine a motor fault signature of the given motor by summing all spectrum components of the current data within the sidebands.

12. The controller of claim 10 wherein the processor is further configured to calculate a root-mean square (RMS) average to sum all the spectrum components of the of the given motor within the sidebands.

13. The controller of claim 10 wherein the processor is further configured to determine a number of poles of the given motor and adjusting the spectrum sideband range based on the number of poles.

14. The controller of claim 1 wherein the processor is further configured to determine a monitoring state of the mapped bin.

15. The controller of claim 14 wherein the processor is further configured to generate the baseline data set by continuously averaging a motor fault signature of the given motor under known healthy conditions if the monitoring state is a learning state.

16. The controller of claim 1 wherein the processor is further configured to determine a noise pattern indicative of potential pump cavitation from the motor fault index of the given motor.

17. The controller of claim 1 wherein the processor is further configured to communicate a proactive alert over a communications bus upon determining a noise pattern indicative of potential pump cavitation.

18. The controller of claim 1 wherein the processor is further configured to receive the current data and power data from a plurality of sensors of a relay.

19. The controller of claim 18 wherein the processor is further configured to:

compile baseline power data;

perform at least one FFT on the operational power data to generate power spectrum data;

generate a motor fault index from power spectrum data;

map a load bin to the power spectrum data;

compare a portion of the baseline power data corresponding to a mapped load bin to the averaged power data; and

determine an impending fault before a fault occurrence.

20. The controller of claim 18 wherein the power data includes instantaneous power data.

21. The controller of claim 1 wherein the processor is further configured to communicate preventative maintenance alerts over a communications bus to an operator display upon detecting indicia of pump cavitation within the motor fault index of the given motor.

22. A method of monitoring a motor for potential faults comprising:

receiving current data from an operating motor;

performing at least one FFT on the current data to generate frequency spectrum data;

isolating sidebands within the frequency spectrum data based on a system frequency of the operating motor;

accumulating spectrum components of the frequency spectrum data within sidebands;

for a predetermined period, generating baseline data from the accumulated spectrum components;

after the predetermined period, comparing the spectrum components to the baseline; and

determining a noise pattern indicative of potential faults due to pump cavitation within the spectrum components.

23. The method of claim 22 further comprising determining a load on an operating motor and mapping the accumulated spectrum components to a load bin based on the load.

24. The method of claim 23 further comprising comparing the spectrum components to a portion of the baseline corresponding to the load bin.

25. The method of claim 22 further comprising dynamically notch filtering the current data received from the operating motor to remove a fundamental frequency of the motor.

26. The method of claim 22 further comprising digitizing and decimating the current data to acquire a desired resolution for comparison with the baseline.

27. The method of claim 22 further comprising performing multiple FFTs on the current data to generate the frequency spectrum data and averaging the multiple FFTs to stabilize the frequency spectrum data.

28. The method of claim 22 further comprising sending a proactive alert indicating an impending pump cavitation fault upon determining a noise pattern indicative of potential faults due to pump cavitation.

29. The method of claim 22 further comprising linearly averaging the frequency components within the sidebands of the system frequency to compile a noise sum within the sidebands of the system frequency.

30. The method of claim 22 wherein the sidebands of the system frequency extend from approximately 25 Hertz (Hz) to 55 Hz and 65 Hz to 90 Hz.

31. The method of claim 22 further comprising receiving the current data from a plurality of sensors of a relay.

32. The method of claim 22 wherein the operating motor is a three-phase motor and the data received is from at least one phase.

33. The method of claim 22 further comprising communicating a proactive alert if a noise pattern indicative of potential pump cavitation is detected through an interface to a communications bus of a system including the operating motor.

34. The method of claim 22 further comprising calculating a root-mean square (RMS) average to accumulate the spectrum components of the frequency spectrum.

35. A computer readable storage medium having stored thereon a computer program comprising instructions which, when executed by at least one processor, cause the at least one processor to:

determine a load on a motor;

receive operational current data from the motor;

perform at least two FFTs on the operational current data to generate frequency spectrum data;

average the frequency spectrum data;

define sidebands of the system frequency of the motor;

sum the frequency spectrum data within sidebands to generate a cavitation fault index;

map the cavitation fault index to a load bin from a plurality of load bins based on the load on the motor;

if in a learning mode, average the cavitation fault index with baseline data associated with the load bin; and

if not in the learning mode, compare the cavitation fault index to the baseline data associated with the load bin to determine an impending cavitation fault before a fault occurrence.

36. The computer readable storage medium of claim 35 wherein the processor is further caused to notch filter the operational current data.

37. The computer readable storage medium of claim 35 wherein the processor is further caused to digitize and decimate the operational current data to acquire a desired resolution for the at least two FFTs.

38. The computer readable storage medium of claim 35 wherein the processor is further caused to determine whether the operational current data is sufficiently stable for the at least two FFTs and if not sufficiently stable, again receive operational current data from the motor.

39. The computer readable storage medium of claim 35 wherein the processor is further caused to send a proactive pump cavitation alert if the impending cavitation fault is detected.