Experimental engineering optimization algorithm at point of performance

• US 20140052959A1
• Filed: 09/16/2010
• Published: 02/20/2014
• Est. Priority Date: 04/09/2010
• 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.
• Application Number:
  US12/807,918
• Publication Number:
  US 20140052959A1
• Time in Patent Office: Days
• US Class Current: 712/7
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• 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

Abstract

A method is provided for reducing the data set used in creating an optimization algorithm, thus to permit the use of microprocessors, that in turn permits embedding the optimization algorithm at the point of performance, in which a subset of data points in a performance window is used to derive a vector that is utilized to create an initial optimization algorithm.

Claims

1. A method of reducing the data sets used by an optimization algorithm, comprising the steps of:

generating a data set for a monitored system, the data set having data points;

utilizing curve fitting techniques to provide a best fit curve for the data points, thus to derive a vector;

utilizing the vector to create an initial optimization algorithm; and

,

embedding the initial optimization algorithm in a microprocessor at the point of performance of the monitored system.

2. The method of claim 1, wherein the best fit curve is that associated with a subset of the data points in a performance window.

3. The method of claim 2, wherein the performance window is a snapshot of data points derived from the monitored system.

4. The method of claim 1, wherein the vector describes the performance of the monitored system.

5. The method of claim 4, wherein the vector establishes coefficients that determine the contribution of each variable to the optimization provided by the optimization algorithm.

6. The method of claim 5, wherein the coefficients established by the vector are used to create the initial optimization algorithm.

7. The method of claim 1, wherein the results from the initial optimization algorithm are transmitted to a remote processor.

8. The method of claim 7, wherein the amount of data transmitted reflects the utilization of the initial optimization algorithm.

9. The method of claim 8, wherein the transmitted data from the initial optimization algorithm is utilized to correct the coefficients associated with the vector used to create the initial optimization algorithm, the coefficients determining the contribution of each variable to the optimization algorithm.

10. The method of claim 9, wherein the output of the initial optimization algorithm is utilized to generate an expanded data set.

11. The method of claim 10, wherein the expanded data set is utilized to correct the initially derived vector, thus to improve the optimization algorithm.