Condition monitoring of an electrohydraulic position control system using artificial neural networks
Pollmeier, K., Burrows, C. R. and Edge, K. A., 2004. Condition monitoring of an electrohydraulic position control system using artificial neural networks. In: ASME International Mechanical Engineering Congress and Exposition, 2006-11-05 - 2006-11-10.
Related documents:This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.
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
|Item Type||Conference or Workshop Items (Paper)|
|Creators||Pollmeier, K., Burrows, C. R. and Edge, K. A.|
|Departments||Faculty of Engineering & Design > Mechanical Engineering|
University Administration & Central Services > Vice-Chancellor's Office
Actions (login required)