Systems and methods for dynamic risk derivation

• US 9,122,253 B2
• Filed: 11/06/2012
• Issued: 09/01/2015
• Est. Priority Date: 11/06/2012
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Block, Frederick William, Mazzaro, Maria Cecilia, Giraldi, Pablo Enrique Acevedo, Sealing, Charles Scott, Runkle, Mark Andrew, Talukdar, Atanu
• Primary Examiner(s): Masinick, Michael D
• Application Number:
  US13/670,435
• Publication Number:
  US 20140129000A1
• Time in Patent Office: 1,029 Days
• Field of Search: 700/29, 700/287, 702/181
• US Class Current: 1/1
• CPC Class Codes: G05B 23/0248 Causal models, e.g. fault tree; digraphs; qualitative physics
• 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: G05B 9/02 electric
• CPC Class Codes: G06F 11/008 Reliability or availability analysis

Abstract

A system may include a dynamic risk calculation engine (DRCE) system. The DRCE includes a model library configured to model a 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. The DRCE further includes a fault tolerance input and a maintenance policy input. The DRCE additionally includes a run-time risk calculation engine 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 dynamic risk calculation engine (DRCE) system comprising;

a model library configured to model a 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;

a run-time risk calculation engine 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; and

a controller having the run-time risk calculation engine, wherein the controller is configured to command a proof test for the apparatus based on the system risk.

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

3. 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.

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

5. The system of claim 1, wherein the run-time risk calculation engine is configured to monitor the proof test to update the system risk.

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

7. The system of claim 1, wherein the system risk comprises an average probability of failure on demand (PFD).

8. 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.

9. The system of claim 1, wherein the run-time risk calculation engine 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.

10. 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, or a combination thereof.

11. 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.

12. A method comprising:

using a dynamic risk calculation engine (DRCE) system to;

use a model library to build a risk model configured to derive a reliability measure comprising a system risk for an apparatus;

use a run-time risk calculation engine to derive the system risk for the apparatus based on the risk model and on an input, wherein the input comprises a fault tolerance input, and a maintenance policy input; and

use a controller having the run-time risk calculation engine to command a proof test for the apparatus based on the 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.

13. The method of claim 12, wherein run-time risk calculation engine comprises a state machine and the risk model comprises an X-out-of-N Markov model configured to use the state machine to model the reliability measure.

14. The method of claim 12, wherein the at least one failure state comprises a controller logic failure state, a turbomachinery failure state, a safety system, or a combination thereof.

15. The method of claim 12, wherein the input comprises a sensor input, wherein the risk calculation engine is configured to derive an action, and wherein the action comprises a maintenance action, an operational action, an economic action, or a combination thereof.

16. A non-transitory machine readable medium comprising instructions configured to:

use a model library to build a risk model configured to derive a reliability measure comprising a system risk for an apparatus;

use a run-time risk calculation engine to derive the system risk for the apparatus based on the risk model and an input, wherein the input comprises a fault tolerance input, and a maintenance policy input; and

use a controller to derive an action command a proof test for the apparatus based on the 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.

17. The non-transitory machine readable medium of claim 16, wherein the controller comprises a single controller, a dual controller, a triple modular redundant (TMR) controller, or a combination thereof.

18. The non-transitory machine readable medium of claim 16, comprising instructions configured to provide a graphical user interface (GUI) configured to build the risk model.

19. The non-transitory machine readable medium of claim 16, 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.