Publication type: Conference paper
Type of review: Peer review (publication)
Title: Online sequential extreme learning machines for fault detection
Authors : Hu, Yang
Fink, Olga
Palme, Thomas
DOI : 10.1109/ICPHM.2016.7542841
Proceedings: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
Conference details: IEEE International Conference on Prognostics and Health Management (ICPHM 2016), Ottawa, Canada, 20-22 June 2016
Issue Date: 2016
Publisher / Ed. Institution : IEEE
ISBN: 978-1-5090-0382-2
978-1-5090-0383-9
Language : English
Subject (DDC) : 004: Computer science
Abstract: In this paper, we propose the application of a new fault detection approach with a sequential updating function under new operating conditions or natural evolving degradation processes. The proposed approach is based on Online Sequential Extreme Learning Machines (OS-ELM). OS-ELM have the advantages of a strong learning ability, very fast training, and online learning. The concept of applying OS-ELM to fault detection is demonstrated on an artificial case study. We expect that OS-ELM can contribute to improve the fault detection and also the associated initiation of maintenance activities for engineering components working in an evolving environment such as electric components, bearings, gears, alternators, shafts and pumps, in which the monitored signals are not only significantly affected by working load and surrounding environment but may also experience some modifications due to a slow aging and degradation process.
URI: https://digitalcollection.zhaw.ch/handle/11475/13908
Fulltext version : Published version
License (according to publishing contract) : Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in Collections:Publikationen School of Engineering

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