|Title:||Online sequential extreme learning machines for fault detection|
|Authors :||Hu, Yang|
|Proceedings:||2016 IEEE International Conference on Prognostics and Health Management (ICPHM)|
|Conference details:||2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, Canada, 20-22 June 2016|
|Publisher / Ed. Institution :||IEEE|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Peer review (Publication)|
|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.|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Publication type:||Conference Paper|
|Appears in Collections:||Publikationen School of Engineering|
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