Hybrid vehicle parameters data collection and analysis for failure prediction and pre-emptive maintenance

• US 20060149519A1
• Filed: 11/14/2005
• Published: 07/06/2006
• Est. Priority Date: 11/15/2004
• Status: Abandoned Application

• Current Assignee: ISE Incorporated (ISE Group of Companies)
• Original Assignee: ISE Incorporated (ISE Group of Companies)
• Inventors: Keller, Jesse P.
• Application Number:
  US11/273,387
• Publication Number:
  US 20060149519A1
• Time in Patent Office: Days
• US Class Current: 703/8
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0858 wherein the data carrier is removable

Abstract

A method of collecting and analyzing large amounts of continuous real time vehicle measurement data from more than 50 monitored parameters includes providing a system for collecting and analyzing large amounts of continuous real time vehicle measurement data from more than 50 monitored parameters; receiving continuous real time vehicle measurement data from more than 50 monitored parameters and filing the data into parameter data logs; analyzing data trends and associations in the vehicle measurement data; identifying subsystem and component failures from the analyzed data trends and associations; classifying and reporting pending failures and failures based on the identified subsystem and component failures; and updating and training the system to recognize new failures and pending failures.

Claims

1. method of collecting and analyzing large amounts of continuous real time vehicle measurement data from more than 50 monitored parameters, comprising:

providing a system for collecting and analyzing large amounts of continuous real time vehicle measurement data from more than 50 monitored parameters;

receiving continuous real time vehicle measurement data from more than 50 monitored parameters and filing the data into parameter data logs;

analyzing data trends and associations in the vehicle measurement data;

identifying subsystem and component failures from the analyzed data trends and associations;

classifying and reporting pending failures and failures based on the identified subsystem and component failures;

updating and training the system to recognize new failures and pending failures.

2. The method of claim 1, wherein the system includes main memory and secondary memory, the secondary memory including one or more of a hard disk drive, a removable data storage drive with a removable data storage medium and an interface to an external data storage medium.

3. The method of claim 1, wherein the system includes one or more processors, the one or more processors including one or more of a coprocessor, a slave processor, a multiple processor system, an input/output processor, a floating point mathematical processor, a special purpose signal processing processor, an auxiliary discrete processor, and an auxiliary integrated processor.

4. The method of claim 1, wherein the system includes a statistical data analysis module to analyze data trends and associations in the vehicle measurement data.

5. The method of claim 1, wherein the system includes a data failure and pending failure identification and classification module using one or more of Bayesian Inference, Regression Analysis, and Artificial Neural Networks.

6. The method of claim 5, wherein the system includes a module to receive continuous real time vehicle measurement data from more than 50 monitored parameters and file the data into parameter data logs;

a module to analyze data trends and associations in the vehicle measurement data;

a module to identify subsystem and component failures from the analyzed data trends and associations;

a module to classify and report pending failures and failures based on the identified subsystem and component failures;

a module to update and train the system to recognize new failures and pending failures, and the update and train module is matched to the identification module and the classification module.

7. A computer-implemented system for collecting and analyzing large amounts of continuous real time vehicle measurement data from more than 50 monitored parameters, comprising:

a module to receive continuous real time vehicle measurement data from more than 50 monitored parameters and file the data into parameter data logs;

a module to analyze data trends and associations in the vehicle measurement data;

a module to identify subsystem and component failures from the analyzed data trends and associations;

a module to classify and report pending failures and failures based on the identified subsystem and component failures;

a module to update and train the system to recognize new failures and pending failures.

8. The system of claim 7, wherein the system includes main memory and secondary memory, the secondary memory including one or more of a hard disk drive, a removable data storage drive with a removable data storage medium and an interface to an external data storage medium.

9. The system of claim 7, wherein the system includes one or more processors, the one or more processors including one or more of a coprocessor, a slave processor, a multiple processor system, an input/output processor, a floating point mathematical processor, a special purpose signal processing processor, an auxiliary discrete processor, and an auxiliary integrated processor.

10. The system of claim 7, wherein the system includes a statistical data analysis module to analyze data trends and associations in the vehicle measurement data.

11. The system of claim 7, wherein the system includes a data failure and pending failure identification and classification module using one or more of Bayesian Inference, Regression Analysis, and Artificial Neural Networks.

12. The system of claim 7, wherein the update and train module is matched to the identification module and the classification module.