SYSTEM AND METHOD FOR PRODUCT DEPLOYMENT AND IN-SERVICE PRODUCT RISK SIMULATION

• US 20100198635A1
• Filed: 02/05/2009
• Published: 08/05/2010
• Est. Priority Date: 02/05/2009
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Foley, Richard, Lall, Nitesh, Baliga, Sandeep, Subrahmanya, Amarnath, Subramonian, Prakash, Sethu, Ramji, Pirtle, Randall
• Granted Patent: US 8,290,802 B2
• Time in Patent Office: Days
• US Class Current: 705/7.41
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06395 Quality analysis or management

Abstract

Methods and systems are provided for predicting the reliability of a plurality of products that will be deployed during a product deployment period comprising a plurality of time intervals. The method comprises retrieving historical product removal data from a product removal data source, retrieving historical product shipment data from a product shipment data source, identifying a plurality of Weibull parameters based on at least a portion of retrieved product removal data and product shipment data, and performing a plurality of product deployment simulations, wherein each product deployment simulation includes predicting a lifespan for each of the plurality of products and determining reliability metrics for uniform time intervals during the product deployment period.

Claims

1. A method for predicting the reliability of a plurality of products that will be deployed during a product deployment period, the method comprising:

retrieving historical product removal data from a product removal data source;

retrieving historical product shipment data from a product shipment data source;

identifying a plurality of Weibull parameters based on at least a portion of retrieved product removal data and product shipment data; and

performing a plurality of product deployment simulations, wherein each product deployment simulation includes predicting a lifespan for each of the plurality of products and determining reliability metrics for uniform time intervals during the product deployment time period.

2. The method of claim 1, wherein the step of identifying the plurality of Weibull parameters further comprises determining a shape parameter and a scale parameter by performing a Weibull analysis on the retrieved product removal data and product shipment data.

3. The method of claim 3, wherein the step of performing further comprises:

a) predicting a product lifespan for each of the plurality of products based on the shape parameter and the scale parameter;

b) determining the total number of operational hours for all deployed products for the uniform time intervals of the product deployment period;

c) determining the total number of product failures for the uniform time intervals of the product deployment period;

e) utilizing a selected base algorithm to determine a product reliability metric for the uniform time intervals of the product deployment period based on the total number of operational hours and the total number of product failures; and

f) repeating steps a through e a plurality of times.

4. The method of claim 3, wherein the step of predicting further comprises predicting a product lifespan for each deployed product, wherein each product lifespan comprises a random failure time that is selected based on the values of the shape parameter and the scale parameter.

5. The method of claim 3, wherein the step of utilizing further comprises utilizing an instantaneous MTBX algorithm to determine the product reliability metrics using CROW-AMSAA Model.

6. The method of claim 3, wherein the step of utilizing further comprises utilizing a moving months average variant of an MTBX algorithm to determine the product reliability metrics.

7. The method of claim 3, wherein the step of utilizing further comprises utilizing a cumulative variant of an MTBX algorithm to determine the product reliability metrics.

8. The method of claim 3, further comprising the steps of:

determining a Mean Time to Failure for the product based on the shape parameter and the scale parameter;

generating a plurality of MTBX Charts based on the reliability metrics;

determining a producer'"'"'s risk of deployment if the Mean Time to Failure for the product is greater than a selected reliability target; and

determining a consumer'"'"'s risk of deployment if the Mean Time to Failure for the product is less than the selected reliability target.

9. The method of claim 8, wherein the step of generating the plurality of MTBX Charts further comprises:

generating a MTBX Chart showing the average value of the reliability metric for each time interval; and

generating a percentage MTBX Chart showing, for each time interval, the percentage of iterations of steps a through e for which the determined reliability metric is less than the selected reliability target.

10. The method of claim 8, wherein the step of generating the plurality of MTBX Charts further comprises:

generating a MTBX Chart showing a percentile value of the reliability metric for each time interval; and

generating a percentage MTBX Chart showing, for each time interval, the percentage of iterations of steps a through e for which the determined reliability metric is less than the selected reliability target.

11. A product deployment and in-service product risk simulation system for predicting the reliability of a plurality of products that will be deployed during a product deployment period, the system comprising:

a product removal data source comprising historical product removal data;

a product shipment data source comprising historical product shipment data;

a processor coupled to the product removal data source and configured to;

retrieve the historical product removal data from the product removal data source;

retrieve the historical product shipment data from the product shipment data source;

identify a shape parameter and a scale parameter by performing a Weibull analysis on the retrieved product removal data and product shipment data;

perform a plurality of product deployment simulations, wherein each product deployment simulation includes predicting a lifespan for each of the plurality of products and utilizing an MTBX algorithm to determine reliability metrics for uniform time intervals during the product deployment period; and

generate at least one MTBX Chart based on the reliability metrics.

12. The system of claim 11, wherein the processor is further configured to:

a) predict a product lifespan for each of the plurality of products based on the shape parameter and the scale parameter, the product life span identifying the amount of time between the shipment of a product and the occurrence of one or more failure events;

b) determine, for the uniform time intervals of the product deployment period, the total number of operational hours of all deployed products;

c) determine, for the uniform time intervals of the product deployment period, the total number of product failures;

e) utilize the MTBX algorithm to determine a product reliability metric for the uniform time intervals of the product deployment period based on the total number of operational hours and the total number of product failures; and

f) repeat a-e a plurality of times.

13. The system of claim 12, wherein the MTBX algorithm comprises an instantaneous MTBX algorithm.

14. The system of claim 12, wherein the MTBX algorithm comprises a moving months variant of the MTBX algorithm.

15. The system of claim 12, wherein the MTBX algorithm comprises a cumulative variant of the MTBX algorithm.

16. The system of claim 12, wherein the processor is further configured to determine a Mean Time to Failure based on the shape parameter and the scale parameter.

17. The system of claim 16, wherein the at least one MTBX Chart comprise:

an MTBX Chart showing the average value of the reliability metrics for each time interval; and

a percentage MTBX Chart showing, for each time interval, the percentage of reliability metrics that were below a selected reliability target.

18. The system of claim 16, wherein the at least one MTBX Chart comprise:

an MTBX Chart showing the percentile value of the reliability metrics for each time interval; and

a percentage MTBX Chart showing, for each time interval, the percentage of reliability metrics that were below a selected reliability target.

19. A computer readable medium having instructions stored thereon that when executed by a processor cause the processor to perform a method for predicting the reliability of a plurality of products that will be deployed during a product deployment period comprising a plurality of time intervals, the method comprising the following steps:

retrieving historical product removal data from a product removal data source;

utilizing a Weibull analysis to determine a scale parameter and a shape parameter for a Weibull distribution that describes at least a portion of the retrieved product removal data;

performing a plurality of product deployment simulations, wherein each product deployment simulation includes predicting a lifespan for each of the plurality of products and utilizing an MTBX algorithm to determine a reliability metrics for uniform time intervals during the product deployment period; and

generating at least one MTBX Chart describing the determined reliability metrics.

20. The computer readable medium of claim 19, wherein the performing step further comprises:

a) predicting a product lifespan for each of the plurality of products based on the shape parameter and the scale parameter;

b) determining the total number of operational hours for all deployed products for the uniform time intervals of the product deployment period;

c) determining the total number of product failures for the uniform time intervals of the product deployment period;

e) utilizing an MTBX algorithm to determine a product reliability metric for the uniform time intervals of the product deployment period based on the total number of operational hours and the total number of product failures; and

f) repeating steps a through e a plurality of times.