A neural network observer for injection rate estimation in common rail injectors with Nozzle wear

Oliver Hofmann, Manuel Kiener, Daniel Rixen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The objective of this study is to present a neural observer that estimates changing injection behavior due to wear and aging effects within the nozzle of a common rail diesel injector. Using a dynamic identification system in combination with a modified learning rule, the neural observer is applicable to a wide range of problem sets. A multilayer perceptron (MLP) network with three layers and few neurons in the hidden layer ensures fast computing and high efficiency; network learning is based on quasi-Newton optimization and an additional line search algorithm. Modeling the bottom part of the injector introduces a simulation model, which is validated with experimental data from a solenoid common rail diesel injector. Estimation results conform well with the altered plant and therefore demonstrate the significant benefit of using the proposed neural network observer concept.

Original languageEnglish
Title of host publicationLecture Notes in Mechanical Engineering
PublisherPleiades journals
Pages277-289
Number of pages13
DOIs
StatePublished - 2018

Publication series

NameLecture Notes in Mechanical Engineering
VolumePartF6
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Keywords

  • Diesel injector
  • Injection rate estimation
  • Injector aging
  • Neural network observer
  • Nozzle wear

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