Misfire Detection Including Confidence Indicators Using a Hardware Neural Network Axeon Limited

Format:

Conference/Event

Author/Creator:

Kirkham, Chris, author.

Conference Name:

SAE 2006 World Congress & Exhibition (2006-04-03 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2006

Summary:

The complexity of automotive power train and control systems is necessitating the implementation of advanced techniques, in turn placing an increasing computational load on the ECU systems.Misfire detection is a pattern classification problem involving the complex, non-linear interactions of good combustion/misfire event distributions for multiple input signals.Building on previously reported developments in these areas, this paper describes practical advances in misfire detection techniques that, through the use of hardware neural network technology, provide measures of "confidence" in the decision, and a single diagnostic metric for arbitration and calibration of the solution for a current series production engine

Notes:

Vendor supplied data

Publisher Number:

2006-01-1349

Access Restriction:

Restricted for use by site license