Publication:
Vibration-based early crack diagnosis with machine learning for spur gears

dc.contributor.buuauthorKarpat, Fatih
dc.contributor.buuauthorKARPAT, FATİH
dc.contributor.buuauthorDirik, Ahmet Emir
dc.contributor.buuauthorDOĞAN, OĞUZ
dc.contributor.buuauthorDİRİK, AHMET EMİR
dc.contributor.buuauthorKalay, Onur Can
dc.contributor.buuauthorKorcuklu, Burak
dc.contributor.buuauthorKORCUKLU, BURAK
dc.contributor.buuauthorDoğan, Oğuz
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentMakine Mühendisliği Bölümü
dc.contributor.orcid0000-0001-8474-7328
dc.contributor.orcid0000-0001-8643-6910
dc.contributor.researcheridKIK-4851-2024
dc.contributor.researcheridA-5259-2018
dc.date.accessioned2024-10-10T06:06:27Z
dc.date.available2024-10-10T06:06:27Z
dc.date.issued2020-01-01
dc.descriptionBu çalışma, Kasım 16-19, 2020 tarihleri arasında düzenlenen ASME International Mechanical Engineering Congress and Exposition (IMECE)’da bildiri olarak sunulmuştur.
dc.description.abstractGear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms.To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms.In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.
dc.description.sponsorshipAmer Soc Mech Engineers
dc.identifier.isbn978-0-7918-8455-3
dc.identifier.urihttps://hdl.handle.net/11452/46171
dc.identifier.wos001233213900004
dc.indexed.wosWOS.ISTP
dc.language.isoen
dc.publisherAmer Soc Mechanical Engineers
dc.relation.journalProceedings Of The Asme 2020 International Mechanical Engineering Congress And Exposition, Imece2020, Vol 7b
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFault
dc.subjectSize
dc.subjectGears
dc.subjectMachine learning
dc.subjectFault diagnosis
dc.subjectDeep learning
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectAutomation & control systems
dc.subjectComputer science, artificial intelligence
dc.subjectEngineering, mechanical
dc.subjectRemote sensing
dc.subjectAutomation & control systems
dc.subjectComputer science
dc.subjectEngineering
dc.subjectRemote sensing
dc.titleVibration-based early crack diagnosis with machine learning for spur gears
dc.typeProceedings Paper
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Makine Mühendisliği Bölümü
local.contributor.departmentMühendislik Fakültesi/Otomotiv Mühendisliği Bölümü
relation.isAuthorOfPublication56b8a5d3-7046-4188-ad6e-1ae947a1b51d
relation.isAuthorOfPublication37bb7eb8-5671-4304-8f09-5f48c51ec56f
relation.isAuthorOfPublication3e5e3219-88a5-4543-976a-263af5fd7b59
relation.isAuthorOfPublicationbd548d68-4fd0-4617-b42a-cdec21ffef07
relation.isAuthorOfPublication.latestForDiscovery56b8a5d3-7046-4188-ad6e-1ae947a1b51d

Files

Collections