METHOD AND APPARATUS FOR PROVIDING ON-BOARD DIAGNOSTICS

• US 20150168950A1
• Filed: 02/24/2015
• Published: 06/18/2015
• 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:
  US14/629,600
• Publication Number:
  US 20150168950A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 2219/2637 Vehicle, car, auto, wheelchair
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

On-board diagnostics for fleet maintenance is improved by embedding a microprocessor programmed with a prognostication algorithm on a vehicle.

Claims

1. A method of providing a computationally less intense prognostication algorithm by providing a stripped down version of a standard prognostication algorithm by using selected input sets and a selected output of a monitored system, determining the probability of the output and developing a prognostication algorithm utilizing modeling and simulation based on an acceptable probability.

3. The method of claim 1, wherein only one selected input and one selected output that describes the operation of the monitored system to determine the acceptable probability.

4. The method of claim 3, wherein the result of the analysis of the single input and the single output is used to develop the prognostication algorithm.

5. The method of claim 1, wherein multiple inputs are utilized with at single output for ascertaining acceptable probability.

6. The method of claim 5, wherein the monitored system has nodes, and wherein when the probability of a node is sufficiently high, the inputs and output are used in he modeling and simulation to develop the prognostication algorithm.

7. The method of claim 3, wherein the selected input and selected output determine the probability at a predetermined node in the monitored system, whereby a reduced set of inputs is utilized in developing the prognostication algorithm.

2. The method of claim 2, and further including the step of embedding the developed prognostication algorithm at the point of performance.