Condition monitoring of an electrohydraulic position control system using artificial neural networks

K Pollmeier, C R Burrows, K A Edge

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

This paper investigates the condition monitoring of a servo-valve-controlled linear actuator system using artificial neural networks (NNs). The aim is to discuss techniques for the identification of failure characteristics and their source. It is shown that neural networks can be trained to identify more than one fault but these are larger and require more training patterns than networks for single fault diagnosis. This leads to much longer training times and to problems with scaleability. Therefore a modular approach has been developed. Several networks were trained each to identify an individual fault. The parallel outputs of these nets were then used as inputs to another network. This additional network was able to identify not only the correct faults but also the actual fault levels. Copyright 2004 by ASME
Original languageEnglish
Pages137-146
Number of pages10
Publication statusPublished - 2004
EventASME International Mechanical Engineering Congress and Exposition - Chicago, IL.
Duration: 5 Nov 200610 Nov 2006

Conference

ConferenceASME International Mechanical Engineering Congress and Exposition
CityChicago, IL.
Period5/11/0610/11/06

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  • Cite this

    Pollmeier, K., Burrows, C. R., & Edge, K. A. (2004). Condition monitoring of an electrohydraulic position control system using artificial neural networks. 137-146. Paper presented at ASME International Mechanical Engineering Congress and Exposition, Chicago, IL., .