Method and system for analysis of turbomachinery

• US 8,996,334 B2
• Filed: 03/02/2011
• Issued: 03/31/2015
• Est. Priority Date: 03/02/2011
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (General Electric Company)
• Original Assignee: General Electric Company
• Inventors: Trejo Sanchez, Adriana Elizabeth, Adhami, Mohammad Waseem, Vega Paez, Jose Leon, Perez Gamboa, Monica Lizbeth, Chavez Valdovinos, Juan Paulo
• Primary Examiner(s): Charioui, Mohamed
• Application Number:
  US13/039,286
• Publication Number:
  US 20120226474A1
• Time in Patent Office: 1,490 Days
• Field of Search: 702/56, 702/58, 702/59, 702/90, 702/179, 702/182, 702/183
• US Class Current: 702/179
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure

Abstract

A method and a system for analyzing turbomachinery is provided. In one embodiment, a system for analyzing turbomachinery is provided. The system includes an intelligent turbomachinery tracking filter (ITTF) system configured to determine one or more performance shifts for one or more components of the turbomachinery based on a plurality of turbomachinery parameters. The system further includes a root cause analyzer configured to determine a root cause of the turbomachinery performance based on the one or more performance shifts. The one or more performance shifts include trended data.

Claims

1. A system for analyzing turbomachinery comprising:

an intelligent turbomachinery tracking filter (ITTF) system configured to determine one or more performance shifts for one or more components of a turbomachinery based on a plurality of the turbomachinery parameters; and

a root cause analyzer configured to determine a root cause of a turbomachinery performance based on the one or more performance shifts, wherein the one or more performance shifts comprise trended data, wherein the root cause analyzer is configured to derive a probability of accuracy of the root cause, and wherein the root cause analyzer comprises a trend quantifier configured to quantify the one or more performance shifts by measuring a maximum value and a minimum value in the one or more of the performance shifts to derive one or more quantified performance shifts.

2. The system of claim 1, wherein the root cause analyzer comprises a fuzzy logic-based engine configured to process the one or more performance shifts using fuzzy logic.

3. The system of claim 1, wherein the root cause analyzer comprises a physics model configured to model a mechanical behavior of the turbomachinery, a thermodynamic behavior of the turbomachinery, or a combination thereof.

4. The system of claim 1, wherein the root cause analyzer comprises a statistical model configured to model historical trends of the turbomachinery.

5. The system of claim 1, wherein the root cause analyzer comprises a knowledge-based system (KBS) having a plurality of knowledge rules, wherein the plurality of knowledge rules are configured to capture knowledge of a subject matter expert on the turbomachinery.

6. The system of claim 5, wherein the KBS comprises a forward chaining system, a backward chaining system, or a combination thereof.

7. The system of claim 1, wherein the turbomachinery comprises at least one of a turbine system, a pump, or a compressor.

8. The system of claim 1, wherein the turbomachinery parameters comprise at least one of a temperature, a vibration, a speed, a flow, a pressure, a fuel measure, a pollution measure, a geometry position, a clearance or an actuator position.

9. A method for analyzing a turbomachinery comprising:

sensing in real-time, via an intelligent turbomachinery tracking filter (ITTF) system communicatively coupled to the turbomachinery, a plurality of sensor signals;

measuring, via the ITTF system, a plurality of turbomachinery parameters by transforming the plurality of sensor signals into the plurality of turbomachinery parameters;

deriving, via the ITTF system, one or more one or more performance shifts for one or more components of the turbomachinery based on the measured turbomachinery parameters;

deriving, via the ITTF system, a profile trend based on the one or more performance shifts;

quantifying, via the ITTF system, the profile trend to produce a quantified profile trend;

deriving, via the ITTF system, a plurality of fuzzy values based on the quantified profile trend; and

deriving, via a root cause analyzer system, a root cause of a turbomachinery performance based on the one or more performance shifts via executing a root cause analysis instructions, wherein the root cause analysis instructions comprise instructions for deriving a probability of accuracy of the root cause, and wherein the root cause analysis instructions comprise instructions for executing a trend quantifier analysis configured to quantify the one or more performance shifts by measuring a maximum value and a minimum value in the one or more of the performance shifts to derive the one or more quantified performance shifts.

10. The method of claim 9, wherein deriving the one or more performance shifts comprises detecting a shift in the quantified profile trend relative to an expected value.

11. The method of claim 10, wherein detecting the shift comprises a Kalman filtering, a tracking filtering, a regression mapping, a neural mapping, an inverse modeling, or a combination thereof, of the measured parameters.

12. The method of claim 9, wherein the quantifying the profile trend to produce a quantified profile trend comprises deriving a slope in the measured parameters, deriving an amount of noise in the measured parameters, or a combination thereof.

13. The method of claim 9, wherein deriving the plurality of fuzzy values comprises mapping a precise value and assigning a degree of truth.

14. The method of claim 9, comprising applying a knowledge based system rule to the quantified profile trend and applying at least one of a physic model or a statistical model to the quantified profile trend to derive the one or more performance shifts.

15. A non-transitory machine readable media, comprising:

instructions configured to process sensor data to identify one or more performance shifts in turbomachinery performance via an intelligent turbomachinery tracking filter (ITTF) system communicatively coupled to a turbomachinery;

instructions configured to derive, via the ITTF system, a profile trend based on the sensor data having the one or more performance shifts in turbomachinery performance;

instructions configured to derive, via the ITTF system, a first root cause of the one or more performance shifts in turbomachinery performance by applying fuzzy logic to the profile trend; and

instructions configured to perform, via a root cause analyzer system, a root cause analysis to derive a probability that the first root cause has been accurately identified, wherein the root cause analysis comprises a trend quantifier analysis configured to quantify the one or more performance shifts by measuring a maximum value and a minimum value in the one or more of the performance shifts to derive the one or more quantified performance shifts.

16. The non-transitory machine readable media of claim 15, wherein the instructions configured to derive, via the root cause analyzer system, the first root cause of the one or more performance shifts in the turbomachinery performance by applying the fuzzy logic comprise instructions for mapping a precise value to a fuzzy value.

17. The non-transitory machine readable media of claim 15, wherein the instructions configured to derive, via the root cause analyzer system, the first root cause of the one or more performance shifts in the turbomachinery performance by applying the fuzzy logic comprise instructions for applying a knowledge based system rule, a physics model, a statistical model, or a combination thereof.

18. The non-transitory machine readable media of claim 15, comprising instructions configured to derive, via the root cause analyzer system, a second root cause of the one or more performance shifts in turbomachinery performance by applying fuzzy logic to the profile trend, and comprising instructions configured to rank the first and the second root causes by assigning a first probability to the first root cause and a second probability to the second root cause, and ranking the first and the second root causes by comparing the first and second probabilities.