Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Clemens Gühmann"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)
Abstract Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and Remaining Useful Life (RUL) prediction. However, accompanied by CNN’s increasing performance is a deeper network structure and growing parameter si
Externí odkaz:
https://doaj.org/article/9ccf2eec67e54f7db1c1be34e957b3cf
Publikováno v:
Lubricants, Vol 11, Iss 12, p 502 (2023)
In recent years, research on bearing fault modeling has witnessed significant advancements. However, the modeling of bearing faults using digital twins (DTs) remains an emerging area of exploration. This paper introduces a bearing digital twin develo
Externí odkaz:
https://doaj.org/article/e36507c854af4a1581af8441152ad95e
Publikováno v:
AI, Vol 2, Iss 3, Pp 444-463 (2021)
Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of distu
Externí odkaz:
https://doaj.org/article/6d42a2de03bb4c5086d03546523661b8
Publikováno v:
Lubricants, Vol 11, Iss 2, p 74 (2023)
A small sample size and unbalanced sample distribution are two main problems when data-driven methods are applied for fault diagnosis in practical engineering. Technically, sample generation and data augmentation have proven to be effective methods t
Externí odkaz:
https://doaj.org/article/0a0c7dd7c2b64879b22cd52fdf4b208a
Autor:
Erik Goldammer, Marius Gentejohann, Michael Schlüter, Daniel Weber, Wolfgang Wondrak, Sibylle Dieckerhoff, Clemens Gühmann, Julia Kowal
Publikováno v:
Batteries, Vol 8, Iss 2, p 11 (2022)
Fast-switching semiconductors induce ripple currents on the high-voltage DC bus in the electric vehicle (EV). This paper describes the methods used in the project SiCWell and a new approach to investigate the influence of these overlaid ripples on th
Externí odkaz:
https://doaj.org/article/c72ac66bef734a7e8440b3b45b19403c
Publikováno v:
Lubricants, Vol 9, Iss 10, p 105 (2021)
Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels ar
Externí odkaz:
https://doaj.org/article/3cb6ce06751f4078b23bae230250b67c
Autor:
Noushin Mokhtari, Jonathan Gerald Pelham, Sebastian Nowoisky, José-Luis Bote-Garcia, Clemens Gühmann
Publikováno v:
Lubricants, Vol 8, Iss 3, p 29 (2020)
In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (
Externí odkaz:
https://doaj.org/article/48260efb2e2b4bf298fc98d64b1a3e20
Publikováno v:
IEEE Access. 11:18868-18886
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2f8b8913f0841b3f00fa180de8f976f
https://doi.org/10.1109/access.2023.3247564
https://doi.org/10.1109/access.2023.3247564
Autor:
José-Luis Bote-Garcia, Clemens Gühmann
Publikováno v:
tm - Technisches Messen. 89:534-543
To develop a system for predicting the remaining useful lifetime of a journal bearing, it is necessary to monitor the progressive wear quantitatively. For this purpose, we create a dataset where the wear volume is tracked throughout several experimen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::591c22f206495432cbc1c35e8d3d6745
https://doi.org/10.1515/teme-2022-0002
https://doi.org/10.1515/teme-2022-0002
Publikováno v:
ATZheavy duty. 14:28-33
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c35162f7348083dd16c9a0b43b75b267
https://doi.org/10.1007/s35746-021-0425-3
https://doi.org/10.1007/s35746-021-0425-3