Computer-implemented methods and systems for determining fleet conditions and operational management thereof

• US 9,552,567 B2
• Filed: 12/27/2012
• Issued: 01/24/2017
• Est. Priority Date: 12/27/2012
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Aerospace)
• Original Assignee: General Electric Company
• Inventors: Iyer, Naresh Sundaram, Varma, Anil, Aragones, James Kenneth, Yan, Weizhong, Bonissone, Piero Patrone, Xue, Feng
• Primary Examiner(s): Hill, Stanley K
• Assistant Examiner(s): Misir, Dave
• Application Number:
  US13/728,378
• Publication Number:
  US 20140188767A1
• Time in Patent Office: 1,489 Days
• Field of Search: 701/29.3, 706/12
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

Claims

1. A method for determining fleet conditions and operational management thereof, wherein said method is performed by a central system having at least one computing device including a processor and a memory device coupled to the processor, said method comprising:

receiving fleet data at the central system from at least one distributed data repository, the fleet data substantially representative of information associated with a fleet of at least two physical assets;

processing, at the central system, the received fleet data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received fleet data into processed fleet data;

determining, at the central system, a fleet condition status for the at least two physical assets using the processed fleet data and the at least one process of the plurality of processes;

generating, at the central system, a fleet response that is substantially representative of a next operational step for the fleet of at least two physical assets, wherein using game theory analysis, the next operational step provides an optimal resource allocation to a first physical asset in the fleet based on resource allocation to other physical assets in the fleet; and

transmitting the fleet response to at least one of a plurality of fleet response recipients.

2. The method in accordance with claim 1, wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of at least one asset associated with an asset type associated with the fleet of physical assets.

3. The method of claim 1, wherein receiving fleet data comprises:

receiving fleet data at the central system from at least one distributed data repository, wherein the received fleet data is one of incomplete or partially complete;

generating estimated fleet data comprising estimating a portion of the fleet data not received based upon previously received fleet data; and

incorporating the estimated fleet data with the received fleet data.

4. The method of claim 1, wherein generating a fleet response comprises:

identifying an availability of parts to maintain the fleet of physical assets;

identifying an availability of human resources for service of the fleet of physical assets; and

applying at least one of a plurality of decision algorithms to determine the next operational step.

5. The method of claim 1, wherein generating a fleet response comprises prioritizing, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition.

6. The method of claim 1, wherein determining a fleet condition status comprises:

determining a first forecast for an availability of parts to maintain the fleet of physical assets;

determining a second forecast for an availability of human resources for service of the fleet of physical assets; and

determining a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.

7. The method of claim 1, wherein determining a fleet condition status includes at least one or a combination of:

analyzing a repository of historical fleet data and historical fleet responses;

creating an expert data model; and

machine learning.

8. The method of claim 1, wherein the fleet condition status includes an indication of at least one of a health assessment, remaining useful life, need for diagnostics, need for maintenance, need for replacement and need for deactivation.

9. A network-based system for determining fleet conditions and operational management thereof for a fleet of at least two physical assets, said system comprising:

a central system having at least one computing device including a processor and a memory device coupled to said processor;

a central database associated with said central system;

a plurality of supplier client devices associated with a plurality of fleet suppliers;

a plurality of supplier databases associated with the plurality of fleet suppliers;

a plurality of servicer client devices associated with a plurality of fleet servicers; and

a plurality of servicer databases associated with the plurality of fleet servicers, said network-based system configured to;

receive fleet data at the central system from at least one distributed data repository, the fleet data substantially representative of information associated with a fleet of at least two physical assets;

process, at the central system, the received fleet data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received fleet data into processed fleet data;

determine, at the central system, a fleet condition status for the at least two physical assets using the processed fleet data and the at least one process of the plurality of processes;

generate, at the central system, a fleet response that is substantially representative of a next operational step for the fleet of at least two physical assets, wherein using game theory analysis the next operational step provides an optimal resource allocation to a first physical asset in the fleet based on resource allocation to other physical assets in the fleet; and

transmit the fleet response to at least one of a plurality of fleet response recipients.

10. The network-based system in accordance with claim 9, wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of at least one asset associated with an asset type associated with the fleet of physical assets.

11. The network-based system of claim 9, wherein said network-based system configured to receive fleet data is further configured to:

receive fleet data at the central system from at least one distributed data repository, wherein the received fleet data is one of incomplete or partially complete;

generate estimated fleet data, comprising estimating a portion of the fleet data not received based upon previously received fleet data; and

incorporate the estimated fleet data with the received fleet data.

12. The network-based system of claim 9, wherein said network-based system configured to generate a fleet response is further configured to:

identify an availability of parts to maintain the fleet of physical assets;

identify an availability of human resources for service of the fleet of physical assets; and

apply at least one of a plurality of decision algorithms to determine the next operational step.

13. The network-based system of claim 9, wherein said network-based system configured to generate a fleet response is further configured to prioritize, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition.

14. The network-based system of claim 9, wherein said network-based system configured to determine a fleet condition is further configured to:

determine a first forecast for an availability of parts to maintain the fleet of physical assets;

determine a second forecast for an availability of human resources for service of the fleet of physical assets; and

determine a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.

15. The network-based system of claim 9, wherein said network-based system configured to determine a fleet condition is further configured to include at least one or a combination of:

analyze a repository of historical fleet data and historical fleet responses;

create an expert data model; and

machine learning.

16. A computer for determining fleet conditions and operational management thereof, said computer comprising a processor and a memory device coupled to said processor, said processor programmed to:

receive fleet data from at least one distributed data repository, the fleet data substantially representative of information associated with a fleet of at least two physical assets;

process the received fleet data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received fleet data into processed fleet data;

determine a fleet condition status for the at least two physical assets using the processed fleet data and the at least one process of the plurality of processes;

generate a fleet response, the fleet response substantially representative of a next operational step for the fleet of at least two physical assets, wherein using game theory analysis the next operational step provides an optimal resource allocation to a first physical asset in the fleet based on resource allocation to other physical assets in the fleet; and

transmit the fleet response to at least one of a plurality of fleet response recipients.

17. The computer in accordance with claim 16, wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of at least one asset associated with an asset type associated with the fleet of physical assets.

18. The computer of claim 16, wherein said processor programmed to receive fleet data is further programmed to:

receive fleet data at a central system from at least one distributed data repository, wherein the received fleet data is one of incomplete or partially complete;

generate estimated fleet data, comprising estimating a portion of the fleet data not received based upon previously received fleet data; and

incorporate the estimated fleet data with the received fleet data.

19. The computer of claim 16, wherein said processor programmed to generate a fleet response is further programmed to:

identify an availability of parts to maintain the fleet of physical assets;

identify an availability of human resources for service of the fleet of physical assets; and

apply at least one of a plurality of decision algorithms to determine the next operational step.

20. The computer of claim 16, wherein said processor programmed to generate a fleet response is further programmed to prioritize, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition.

21. The computer of claim 16, wherein said processor programmed to determine a fleet condition status is further programmed to:

determine a first forecast for an availability of parts to maintain the fleet of physical assets;

determine a second forecast for an availability of human resources for service of the fleet of physical assets; and

determine a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.