Variable-structure diagnostics approach achieving optimized low-frequency data sampling for EMA motoring subsystem

• US 7,081,729 B2
• Filed: 03/23/2004
• Issued: 07/25/2006
• Est. Priority Date: 03/23/2004
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Chang, Jie, Zhang, Jiajia, Wang, Anhua, Keller, Kirby
• Primary Examiner(s): Ro, Bentsu
• Application Number:
  US10/807,645
• Publication Number:
  US 20050212523A1
• Time in Patent Office: 854 Days
• Field of Search: 318/138, 318/254, 318/439, 318/561, 318/562, 318/565, 318/568.24, 318582-586, 318/611, 318/623, 318/632, 388/907.5, 388/909, 244/194, 244/195, 244/226, 244/227, 244/230
• US Class Current: 318/565
• CPC Class Codes: G01R 31/008 on air- or spacecraft, railway rolling stock or sea-going vessels
• CPC Class Codes: G01R 31/2829 Testing of circuits in sensor or actuator systems testing of apparatus for measuring electric or magnetic variables G01R35/00; testing of indicating or recording apparatus G01D; in airbag systems B60R21/0173; checking gas analysers G01N33/007; monitoring or fail-safe circuits for electromagnets H01F7/1844
• CPC Class Codes: G01R 31/2834 Automated test systems [ATE]; using microprocessors or computers G01R31/317 takes precedence; ATE for detection of defective computer hardware G06F11/2736
• CPC Class Codes: G01R 31/2846 using hard- or software simulation or using knowledge-based systems, e.g. expert systems, artificial intelligence or interactive algorithms
• CPC Class Codes: Y10S 706/913 Vehicle or aerospace

Abstract

The present invention provides a diagnostics methodology and embedded electronic system that allows optimized low-frequency data sampling for EMA motoring subsystems in an operating vehicle. Each of the EMA motoring subsystems includes: an EMA; at least one motor for driving the EMA; and power controls for operating the motor, wherein the power controls includes a DSP controller for sampling and processing data at low-frequency sampling rates. The diagnostic methodology includes a method that has the steps of: determining an operational mode of the EMA motoring subsystem; selecting a sampling rate optimized for the determined operational mode; acquiring and processing data at the selected sampling rate; and analyzing the processed data to identify and classify a fault of the EMA motoring subsystem.

Claims

1. A method for performing an integrated diagnostics of an EMA motoring subsystem using embedded electronic control circuits, the method comprising:

implementing a plurality of operating structures into an EMA motoring subsystem, each operating structure having optimized rates of data sampling and processing;

determining an operational mode of the EMA motoring subsystem;

selecting one of the implemented plurality of operating structures that corresponds to the determined operational mode;

acquiring multi-channel data using the selected operating structure; and

analyzing the acquired data to identify and classify a fault of the EMA motoring subsystem.

2. A method of variable structure diagnostics system, comprising:

implementing a plurality of operating structures into an EMA motoring subsystem, each operating structure having optimized rates of data sampling and processing;

determining an operational mode of the EMA motoring subsystem;

selecting one of the implemented plurality of operating structures that corresponds to the determined operational mode;

acquiring multi-channel data using the selected operating structure; and

analyzing the acquired data to identify and classify a fault of the EMA motoring subsystem.

3. The method of claim 2, wherein the determined preferred operational mode of a low sampling frequency is a pseudo-small-signal mode.

4. The method of claim 2, wherein the determined operational mode is a large signal mode in complementary to the pseudo-small-signal mode.

5. The method of claim 2 identifies the vehicle mission states combining with the estimation of the pseudo-small-signal mode, further reducing the rate of the data sampling and processing.

6. A method for performing integrated diagnostics of an EMA motoring subsystem in a flying vehicle at a low-frequency sampling rate, the method comprising:

determining a mission phase of an operating vehicle;

selecting a pseudo-steady-state operating condition of an EMA motoring subsystem of the operating vehicle based on the determined mission phase;

sampling and processing data at a low-frequency sampling rate optimized for the selected pseudo-steady-state operating condition; and

identifying and classifying a fault of the EMA motoring subsystem based on the processed data.

7. The method of claim 6, wherein the determined mission phase is on-ground, take-off, cruise or landing phase.

8. The method of claim 6, further including the step of:

predicting the fault of the EMA motoring subsystem based on the processed data.

9. The method of claim 6, wherein the operating vehicle is an Unmanned Combat Air Vehicle (UCAV), a shuttle evolved vehicle or new manned space vehicle, a commercial aircraft, a land based autonomous craft, a land based manned craft, a sea based autonomous craft or a sea based manned craft.

10. A method for performing integrated diagnostics and prognostics of an EMA motoring subsystem in a vehicle, the method comprising:

monitoring an operational status of an EMA motoring subsystem of a vehicle;

determining an operational mode of the EMA motoring subsystem based on key state variables of the EMA motoring subsystem and a mission phase of the vehicle;

selecting sampling rate optimized for the determined operational mode;

acquiring multi-channel data at the selected sampling rate;

storing the acquired data to a memory through a DMA channel;

analyzing condition of the EMA motoring subsystem using the stored data;

identifying and classifying a fault of the EMA motoring subsystem based on the analyzed condition; and

predicting the fault of the EMA motoring subsystem based on the analyzed condition.

11. A method of claim 10, wherein the determined operational mode is a pseudo-small-signal mode or a large signal mode.

12. A method of claim 10, wherein the selected sampling rate is an optimum sampling rate for the large signal mode.

13. A method of claim 10, wherein the EMA motoring subsystem is a stator winding, a rotor bar or a bearing system.

14. A method for performing integrated diagnostics and prognostics of an EMA motoring subsystem in a vehicle, the method comprising:

implementing a plurality of operating structures into an EMA motoring subsystem of a vehicle, each operating structure having a data sampling rate;

monitoring an operational status of the EMA motoring subsystem;

determining a flight mission phase of the vehicle;

assessing key state variables of the EMA motoring subsystem;

selecting one of the plurality of operating structures based on assessed key state variables and the determined flight mission phase;

acquiring multi-channel data using the selected operating structure;

storing the acquired data to a memory through a DMA channel;

analyzing condition of the EMA motoring subsystem using the stored data;

identifying and classifying a fault of the EMA motoring subsystem based on the analyzed condition; and

predicting the fault of the EMA motoring subsystem based on the analyzed condition.