Telenostics performance logic

• US 20140052499A1
• Filed: 05/25/2010
• Published: 02/20/2014
• Est. Priority Date: 02/23/2009
• Status: Abandoned Application

• Current Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc)
• Original Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc)
• Inventors: Wagner, Ronald E., Charlton, Robert, Ufford, Robert, Thompson, Greg
• Application Number:
  US12/800,908
• Publication Number:
  US 20140052499A1
• Time in Patent Office: Days
• US Class Current: 705/7.36
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

In a method for managing a vehicle fleet using operational and maintenance data, wherein the improvement comprises the steps of also using financial, environmental, and industry data.

Claims

1. A dynamic system for managing a vehicle fleet using operational and maintenance data, the vehicle fleet comprising one or more vehicles, the system comprising:

a data center receiving real-time data corresponding to at least the vehicle fleet; and

a reliability centered maintenance module coupled to the data center, the reliability centered maintenance module being executed by a processor;

the processor configured to;

execute a model-based adaptive diagnostics, wherein the model-based adaptive comprises one or more model-based diagnostics algorithms for detecting one or more faults and one or more causes of the one or more faults, wherein at least one of the one or more model-based diagnostics algorithms is updated based on the real-time data through adaptive learning;

execute a model-based adaptive prognostics, the model-based adaptive prognostics comprising one or more model-based prognostics algorithms for predicting one or more expected faults and one or more refined causes of the one or more expected faults, wherein at least one of the model-based prognostics algorithms is updated based on the real-time data through adaptive learning or through an input from the at least one of one or more updated model-based diagnostics algorithms; and

execute a financial module for calculating one or more computation parameters based on the one or more expected faults and the one or more refined causes, the computational parameters comprises at least one of cost of doing a repair of one or more parts, cost of buying of the one or more parts, cost of transport of the one or more parts to a repair facility and downtime costs;

characterized in that the processor facilitates adjustment of an original vehicle fleet maintenance plan for generating a revised vehicle fleet maintenance plan based on at least the model-based adaptive diagnostics, the model-based adaptive prognostics, and the one or more computational parameters.

3. The system of claim 1, wherein the financial module comprises a performance logic module that uses as inputs operating labor costs, maintenance labor costs, fuel costs, tire costs, consumable liquid costs, preventive maintenance costs, corrective maintenance costs, administrative costs, security costs, network and communication costs, insurance costs, safety costs, and litigation costs.

4. The system of claim 3, wherein the performance logic module is presented with the real-time data relating to a location of the one or more vehicles, historical down time cost, including cost associated with lost productivity, and operating condition of the one or more vehicles.

5. The system of claim 3, wherein the performance logic module prioritizes information relating to the cost of doing the repair versus cost of buying of the one or more parts in real-time.

6. The system of claim 3, wherein the performance logic module is provided with historical maintenance and repair information including faults that have occurred or are about to occur for the one or more parts.

7. The system of claim 3, wherein the performance logic module provides a recommendation to either immediately effectuate a repair or to wait to make the repair of the one or more parts.

8. The system of claim 3, wherein the performance logic module provides information that enables a fleet manager to marshal one or more resources necessary as to the best place for a repair, where the one or more vehicles are located, whereby a repair authorization from the fleet manager enables the fleet manager to have a right technician available at a specific place and time, thus to permit scheduling of the repair.

9. The system of claim 1, wherein the processor takes into account static and dynamic information of the one or mere vehicles of the vehicle fleet.

10. The system of claim 9, wherein the static information is accumulated as to the historical cost of one or more parts.

11. The system of claim 9, wherein the static information includes historical repair costs.

12. The system of claim 11, wherein the repair costs includes how much time it takes for a particular repair.

13. The system of claim 12, wherein the cost to make the particular repair is predicated on an hourly rate of a maintenance technician.

15. The system of claim 1, wherein the processor is configured to execute a module fore projecting cost avoidance including the cost history associated with a decision that is made as to when to take the one or more vehicles out of service or when to make a repair, also including costs which are to be avoided by making a prompt repair.

2. (canceled)

14. (canceled)