Systems and methods for improved reliability operations

• US 9,158,303 B2
• Filed: 03/27/2012
• Issued: 10/13/2015
• Est. Priority Date: 03/27/2012
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Mazzaro, Maria Cecilia, Block, Frederick William, Acevedo Giraldi, Pablo Enrique, Sealing, Charles Scott, Talukdar, Atanu, Runkle, Mark Andrew
• Primary Examiner(s): Rivas, Omar Fernandez
• Assistant Examiner(s): MOLL, NITHYA JANAKIRAMAN
• Application Number:
  US13/431,914
• Publication Number:
  US 20130262064A1
• Time in Patent Office: 1,295 Days
• Field of Search: None
• 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

Abstract

A system may include a model library 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. 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; and

wherein the proof test is configured to automatically actuate a valve, a backup system, an apparatus component, or a combination thereof; and

to output a proof test schedule.

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 safety system comprises a safety instrumented function (SW).

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 comprises a partial proof test or a full proof test.

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

7. 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.

8. 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.

9. 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. The method of claim 9, wherein each of the plurality of subsystem models comprises an X-out-of-N Markov model configured to use a plurality of states and state transitions to model the reliability measure.

12. The method of claim 9, comprising monitoring the system via the processor, wherein the monitoring is used to update the plurality of state probabilities for the system.

13. The method of claim 9, wherein the proof test comprises a partial proof test, a full proof test, an inspection, a component refurbishment, a component replacement, or a combination thereof.

10. A method comprising:

predicting, via a processor, a plurality of state probabilities for a system based on a model library, 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;

computing, via the processor, a system probability of failure on demand (PFD);

deriving, via the processor, an average PFD based on the system PFD;

if an N-step ahead estimate of the average PFD exceeds a risk reduction factor (RRF), then commanding, via the processor, a proof test;

wherein the proof test is configured to automatically actuate a valve, a backup system, an apparatus component, or a combination thereof; and

outputting, via the process, a proof test schedule.

14. 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;

predict a plurality of state probabilities for a system based on a model library, 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;

compute a system probability of failure on demand (PFD);

derive an average PFD based on the system PFD;

if an N-step ahead estimate of the average PFD exceeds a risk reduction factor (RRF), then commanding a proof test

wherein the proof test is configured to automatically actuate a valve, a backup system, an apparatus component, or a combination thereof; and

output a proof test schedule.

15. The system of claim 14, wherein the controller comprises a single controller, a dual controller, a triple modular redundant (TMR) controller, or a combination thereof.

16. The system of claim 14, wherein the controller comprises a graphical user interface (GUI) configured to graphically display the plurality of subsystem models, and wherein a user-selected set of the plurality of subsystem models may be connected in series, in parallel, or a combination thereof, to construct a safety instrumented function (SIF).

17. The system of claim 14, 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.