Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions

Ming Zhang, Weining Lu, Jun Yang, Duo Wang, Bin Liang

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Deep learning has been widely developed to solve fault diagnosis issues, and it is becoming a crucial technology in the modern manufacturing industry. As an important transmission device of mechanical equipment, gearbox often runs at different speeds and loads, which may lead to changes in data distribution for the actual application. The cross-domain problem caused by the different data distribution may decline the performance of the fault diagnosis model based on deep learning. To overcome this challenge, a new domain adaptation method, named MAAN: Multilayer Adversarial Adaptation Networks, for fault diagnosis of gearbox running at multiple operating conditions. The basic framework of our MAAD is a deep convolutional neural network (CNN) and then an adversarial adaptation learning procedure is used for optimizing the basic CNN to adapt cross different domain. The results of the experiment demonstrate that MAAN has outstanding fault diagnosis and domain adaptation capacity, and it could obtain high accuracies for fault diagnosis of the gearbox with changing mode. For investigating the adaptability in this method, we use t-SNE to reduce the high dimension feature for better visualization.
Original languageEnglish
Title of host publication2019 Prognostics and System Health Management Conference (PHM-Qingdao)
Place of PublicationQingdao, China,
PublisherIEEE
Pages1-6
Volume2019
ISBN (Electronic)978-1-7281-0861-2
ISBN (Print)978-1-7281-0862-9
DOIs
Publication statusPublished - 26 Dec 2019
Event2019 Prognostics and System Health Management Conference (PHM-Qingdao) - Qingdao, China
Duration: 25 Oct 201927 Oct 2019

Conference

Conference2019 Prognostics and System Health Management Conference (PHM-Qingdao)
CountryChina
CityQingdao
Period25/10/1927/10/19

Fingerprint

Dive into the research topics of 'Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions'. Together they form a unique fingerprint.

Cite this