METHODS, SYSTEMS, AND COMPUTER PROGRAM PRODUCTS FOR IMPLEMENTING CONDITION MONITORING ACTIVITIES

• US 20060174164A1
• Filed: 02/01/2005
• Published: 08/03/2006
• Est. Priority Date: 02/01/2005
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Bonissone, Piero Patrone, Yan, Weizhong, Hershey, John Erik, Goebel, Kai Frank, Iyer, Naresh Sundaram, Hatch, Charles Terrance, Tomlinson, Harold Woodruff Jr.
• Application Number:
  US10/906,052
• Publication Number:
  US 20060174164A1
• Time in Patent Office: Days
• US Class Current: 714/47
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms

Abstract

Methods, systems, and computer program products are provided for implementing condition monitoring activities. Systems include a processor in communication with a machine being monitored. The processor receives signals output by the machine via a signal conversion element associated with the machine. Systems also include a display device in communication with the processor for providing signatures of the signals received from the signal conversion element. Systems further include a means for identifying, isolating, and capturing a signature from the signatures presented on the display device. The systems also include a means for digitizing and recording the signature as an event kernel, normalizing the event kernel by performing a mean removal, and normalizing the energy to unity on results of the mean removal. Systems further include a storage device for storing normalized event kernels.

Claims

1. A system for implementing condition monitoring activities, comprising:

a processor in communication with a machine being monitored, the processor receiving signals output by the machine via a signal conversion element associated with the machine;

a display device in communication with the processor, the display device providing signatures of the signals received from the signal conversion element;

a means for identifying, isolating, and capturing a signature from the signatures presented on the display device;

a means for digitizing and recording the signature as an event kernel;

a means for normalizing the event kernel by performing a mean removal and normalizing the energy to unity on results of the performing a mean removal; and

a storage device for storing normalized event kernels.

2. The system of claim 1, wherein the signature is identified, isolated, and captured as an angular interval over a 360-degree machine cycle.

3. The system of claim 1, wherein the capture further includes at least one of:

performing band-pass or high-pass filtering on the signature for improving performance of event localization and extracting the signature from the signatures presented on the display device; and

extracting the signature from the signatures presented on the display device, the signatures comprising waveforms reflected from machine parts upon interaction with excitation waveforms radiated into the machine.

4. The system of claim 1, the event kernel represented as S=(s1, s2, . . . , sn), wherein S is the event kernel, s represents a signature sample and n represents a number of signature samples.

5. The system of claim 4, wherein the mean removal is represented as

S←

S−

{overscore (s)}

and the normalizing the energy to unity on results of the performing a mean removal is represented as

6. The system of claim 1, further comprising:

an other display device in communication with the storage device and the processor,

wherein the storage device further stores operational data associated with the machine; and

a means for performing at least one of;

computing an autocorrelation on the normalized event kernel; and

computing a cross-correlation on the normalized event kernel against the operational data.

7. The system of claim 6, wherein the autocorrelation and cross-correlation are performed via convolution in the Fourier domain.

8. The system of claim 6, further comprising a means for:

evaluating repeatability of the event kernel over the machine within the same machine state by performing a sliding cross-correlation computation of the normalized event kernel against an event kernel associated with an other trace;

presenting on the other display device a time of occurrence of the event kernel within the other trace, as the time where a cross-correlation plot has a peak value; and

displaying in response to a user-specific threshold value, whether or not the event kernel is identified within the other trace by using the user-specific threshold on the cross-correlation plot for revealing any values that exist which are greater than the user-specific threshold.

9. The system of claim 8, further comprising a means for evaluating results of the evaluating repeatability, the evaluating results comprising:

collecting a set of normalized event kernels from the storage device that are the same as a normalized event kernel identified; and

computing averages on the set of normalized event kernels.

10. The system of claim 9, further comprising a means for evaluating results of the evaluating repeatability, the evaluating results comprising:

collecting a set of normalized event kernels from the storage device that are the same as a normalized event kernel identified; and

computing a variance of the set of normalized event kernels against the normalized event kernel identified.

11. The system of claim 1, wherein the machine is a turbine engine.

12. The system of claim 1, wherein the signal conversion element is at least one of a transducer and a shaft encoder.

13. The system of claim 1, wherein the signals output by the machine are sampled via passive ultrasonic sensing and the signature is presented in a power spectral density plot on the display device.

14. The system of claim 1, further comprising an analyzer for performing active acoustic sensing of signals, the analyzer comprising:

a transmitter module generating acoustic waveforms applied to cabled active acoustic transducers, the active acoustic transducers coupled to housing of the machine, the active acoustic sensing comprising;

radiating excitation signals into the machine via the active acoustic transducers, the excitation signals interacting with moving parts of the machine;

modifying reflections of the excitation signals resulting from the interacting, the modifying reflections resulting in secondary signals; and

conducting the secondary signals through the housing for sampling, the sampling performed by passive acoustic transducers coupled to the machine.

15. A method for implementing condition monitoring activities, comprising:

receiving signals output by a machine being monitored;

isolating and capturing a signature from the signals;

digitizing and recording the signature as an event kernel; and

normalizing the event kernel by performing a mean removal and normalizing the energy to unity on results of the performing a mean removal.

16. The method of claim 15, further comprising at least one of:

computing an autocorrelation on the normalized event kernel; and

computing a cross-correlation on the normalized event kernel against operational data associated with the machine.

17. The method of claim 16, further comprising:

evaluating repeatability of the event kernel over the machine within the same machine state by performing a sliding cross-correlation computation of the normalized event kernel against an event kernel associated with an other trace;

presenting on a display device a time of occurrence of the event kernel within the other trace, as the time where a cross-correlation plot has a peak value; and

displaying in response to a user-specific threshold value, whether or not the event kernel is identified within the other trace by using the user-specific threshold on the cross-correlation plot for revealing any values that exist which are greater than the user-specific threshold.

18. The method of claim 17, further comprising evaluating results of the evaluating repeatability, the evaluating results comprising:

collecting a set of normalized event kernels from the storage device that are the same as a normalized event kernel identified; and

computing averages on the set of normalized event kernels.

19. The method of claim 17, further comprising evaluating results of the evaluating repeatability, the evaluating results comprising:

collecting a set of normalized event kernels from the storage device that are the same as a normalized event kernel identified; and

computing a variance of the set of normalized event kernels against the normalized event kernel identified.

20. The method of claim 15, wherein the machine is a turbine engine.