An Ensemble Approach for Fault Diagnosis via Continuous Learning

Dapeng Zhang, Zhiwei Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 19th International Conference on Industrial Informatics (INDIN)
Place of PublicationPiscataway, US
PublisherIEEE
Number of pages5
ISBN (Electronic)9781728143958
ISBN (Print)9781728143965
DOIs
Publication statusPublished - 21 Jul 2021
Event19th IEEE International Conference on Industrial Informatics, INDIN 2021 - Mallorca, Spain
Duration: 21 Jul 202123 Jul 2021

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2021-July
ISSN (Print)1935-4576

Conference

Conference19th IEEE International Conference on Industrial Informatics, INDIN 2021
Country/TerritorySpain
CityMallorca
Period21/07/2123/07/21

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