VTechWorks staff will be away for the Independence Day holiday from July 4-7. We will respond to email inquiries on Monday, July 8. Thank you for your patience.
 

A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction

Simple item page
dc.contributor.authorLi, Zhenen
dc.contributor.authorLi, Yongxiangen
dc.contributor.authorYue, Xiaoweien
dc.contributor.authorZio, Enricoen
dc.contributor.authorWu, Jianguoen
dc.date.accessioned2023-02-06T20:04:51Zen
dc.date.available2023-02-06T20:04:51Zen
dc.date.issued2022-08en
dc.date.updated2023-02-05T03:37:32Zen
dc.description.abstractIn complex systems, the operating units often suffer from multiple failure modes, and each failure mode results in distinct degradation path and service life. Thus, it is critical to perform the failure mode diagnostics and predict the remaining useful life (RUL) accordingly in modern industrial systems. However, most of the existing approaches consider the prognostic problem under a single failure mode or treat the failure mode classification and RUL prediction as two independent tasks, despite the fact that they are closely related and should be synergistically performed to enhance the generalization performance. Motivated by these issues, we propose a deep branched network (DBNet) for failure mode classification and RUL prediction. In this approach, the two tasks are jointly learned in a sequential manner, in which the feature extraction layers are shared by both tasks, while the neural network branches into individualized subnetworks for RUL prediction of each mode based on the output of the diagnostic subnetwork. Different from the traditional multitask learning-based methods, where the diagnostics and RUL prediction are performed in parallel, the proposed DBNet innovatively couples these two tasks sequentially to boost the prognostic accuracy. The effectiveness of the proposed method is thoroughly demonstrated and evaluated on an aircraft gas turbine engine with multiple failure modes.en
dc.description.versionAccepted versionen
dc.format.extentPages 1-11en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TIM.2022.3195280en
dc.identifier.eissn1557-9662en
dc.identifier.issn0018-9456en
dc.identifier.orcidYue, Xiaowei [0000-0001-6019-0940]en
dc.identifier.urihttp://hdl.handle.net/10919/113686en
dc.identifier.volume71en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleA Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Predictionen
dc.title.serialIEEE Transactions on Instrumentation and Measurementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
J28-A_Deep_Branched_Network_for_Failure_Mode_Diagnostics_and_Remaining_Useful_Life_Prediction.pdf
Size:
12.09 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Industrial and Systems Engineering