Systems and methods for improved reliability operations

• US 9,280,617 B2
• Filed: 11/06/2012
• Issued: 03/08/2016
• Est. Priority Date: 11/06/2012
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Mazzaro, Maria Cecilia, Block, Frederick William, Giraldi, Pablo Enrique Acevedo, Sealing, Charles Scott, Talukdar, Atanu, Runkle, Mark Andrew
• Primary Examiner(s): Le, John H
• Application Number:
  US13/670,420
• Publication Number:
  US 20140129187A1
• Time in Patent Office: 1,218 Days
• Field of Search: 702/181, 702/183, 702/184, 702/185, 700/29, 700/79, 700/287, 703/6
• US Class Current: 1/1
• CPC Class Codes: G05B 23/0251 Abstraction hierarchy, e.g. "complex systems", i.e. system is divided in subsystems, subsystems are monitored and results are combined to decide on status of whole system
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A system may include a model library configured to model a system, wherein the model library includes a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The system further includes a fault tolerance input and a maintenance policy input. The system further includes a dynamic risk calculation engine (DRCE) configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input and the maintenance policy input, to derive a system risk for an apparatus.

Claims

1. A system comprising:

a processor communicatively coupled to a memory;

a model library stored in the memory and configured to model a safety system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure;

a fault tolerance input;

a maintenance policy input; and

a dynamic risk calculation engine (DRCE) configured to be executed by the processor and configured to;

use a user-defined set of the plurality of subsystem models, the fault tolerance input, and the maintenance policy input, to derive a system risk for an apparatus, wherein the system risk comprises an average probability of failure on demand (PFD);

determine if an N-step ahead estimate of the average PFD exceeds a risk reduction factor (RRF), and if the N-step ahead estimate exceeds the RRF, then to command a proof test.

2. The system of claim 1, wherein each of the plurality of subsystem models comprises an X-out-of-N (XooN) Markov model configured to use a plurality of states and state transitions to model the reliability measure.

3. The system of claim 1, wherein the fault tolerance input comprises a risk reduction factor (RRF), and the system comprises a turbomachinery, a safety system, or a combination thereof.

4. The system of claim 1, wherein the maintenance policy input comprises an online diagnosis input, an output diagnosis input, a proof test model input, a full refurbishment input, or a combination thereof.

5. The system of claim 1, wherein the proof test is configured to automatically actuate a valve, a backup system, an apparatus component, or a combination thereof.

6. The system of claim 1, wherein the proof test comprises a partial proof test or a full proof test.

7. The system of claim 1, wherein the DRCE is configured to monitor the proof test to update the system risk.

8. The system of claim 1, wherein the DRCE is configured to use an alarm, a partial proof test, a full proof test, an operational event, a maintenance event, or a combination thereof, to derive a reliability credit, and wherein the reliability credit is used to update the system risk.

9. The system of claim 1, wherein the apparatus comprises a gas turbine system, a steam turbine system, a wind turbine system, a nuclear reactor system, a combustion engine, an electric motor, a pneumatic motor, a hydraulic engine, a device, or a combination thereof.

10. The system of claim 1, wherein the system risk is configured to be provided as input to an optimization system for the derivation of a maintenance action, an operational action, an economic action, an environmental action, or a combination thereof.

11. A method comprising:

using, via a processor, a model library to build a risk model;

deriving, via the processor, a reliability measure based on the risk model and on an input;

deriving, via the processor, an action based on the reliability measure and on a system risk, wherein the risk model comprises at least one failure state, one okay state, at least one derated state, at least one repair rate transition, and at least one failure rate transition, wherein the system risk comprises an average probability of failure on demand (PFD) and wherein the reliability measure comprises a risk reduction factor (RRF); and

determining, via the processor, if an N-step ahead estimate of the average PFD exceeds a risk reduction factor (RRF), and if the N-step ahead estimate exceeds the RRF, then to command a proof test.

12. The method of claim 11, wherein the risk model comprises an X-out-of-N Markov model configured to use the at least one failure state, the one okay state, the at least one derated state, the at least one repair rate transition, and the at least one failure rate transition to model the reliability measure.

13. The method of claim 11, wherein the at least one failure state comprises a controller logic failure state.

14. The method of claim 11, wherein the input comprises a fault tolerance input, a maintenance policy input, a sensor input, or a combination thereof, and wherein the action comprises a maintencance action, an operational action, an economic action, or a combination thereof.

15. A system comprising:

a controller configured to control a machine system and to receive inputs from the machine system, wherein the controller comprises non-transitory machine readable instructions configured to;

use a model library to build a risk model;

derive a reliability measure based on the risk model, a system risk, and an input;

derive an action based on the reliability measure, wherein the risk model comprises at least one failure state, one okay state, at least one derated state, at least one repair rate transition, and at least one failure rate transition, wherein the system risk comprises an average probability of failure on demand (PFD) and wherein the reliability measure comprises a risk reduction factor (RRF); and

determine if an N-step ahead estimate of the average PFD exceeds a risk reduction factor (RRF), and if the N-step ahead estimate exceeds the RRF, then to command a proof test.

16. The system of claim 15, wherein the controller comprises a single controller, a dual controller, a triple modular redundant (TMR) controller, or a combination thereof.

17. The system of claim 15, wherein the controller comprises a graphical user interface (GUI) configured to build the risk model.

18. The system of claim 15, wherein the risk model comprises an X-out-of-N (XooN) Markov model configured to use the at least one failure state, the one okay state, the at least one derated state, the at least one repair rate transition, and the at least one failure rate transition to model the reliability measure.