1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks
Συγγραφέας:	[EL] Καρναβάς, Ιωάννης[EN] Karnavas, Yannis [EL] Πλακιάς, Σπυρίδων[EN] Plakias, Spyridon [EL] Χασιώτης, Ιωάννης-Ειρηναίος[EN] Chasiotis, Ioannis-Eirinaios
Ημερομηνία:	22/03/2021
Περίληψη:	A health diagnosis mechanism of rolling element bearings is necessary since the most frequent faults in rotating electrical machines occur in the bearing parts. Recently, convolutional neural networks (CNNs) have redefined the state-of-the-art accuracy for bearing fault detection and identification, extracting location invariant feature mappings without the need for prior expert knowledge. With the use of convolution operations as the core of the process, CNNs consider the local spatial coherence of the input. However, one major drawback of the convolutional models is the weakness to capture global information about the input vector and to derive knowledge about the statistical properties of the latter. The authors propose a deep learning (DL) model that concatenates the features that are produced from two neural streams. Each consists of an attention mechanism that intends to learn different representations of the input vector, and so finally to produce a feature mapping that contains global and spatial locally information. Simulation results on two famous rolling element bearings fault detection benchmarks show the effectiveness of the method. In particular, the proposed DL model achieves 99.60% in the Case Western Reserve University bearing data set and 99.10% in the Paderborn University bearing data set.
Γλώσσα:	Αγγλικά
Σελίδες:	13
DOI:	10.1049/elp2.12063
EISSN:	1751-8679
Θεματική κατηγορία:	[EL] Τεχνητή νοημοσύνη[EN] Artificial Intelligence
Κάτοχος πνευματικών δικαιωμάτων:	© 2021 The Authors. IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
Όροι και προϋποθέσεις δικαιωμάτων:	This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/elp2.12063
Ηλεκτρονική διεύθυνση περιοδικού:	https://ietresearch.onlinelibrary.wiley.com/journal/17518679
Τίτλος πηγής δημοσίευσης:	IET Electric Power Applications
Τεύχος:	7
Τόμος:	15
Σελίδες τεκμηρίου (στην πηγή):	903-915
Σημειώσεις:	This research is co-financed by Greece and the European Union (European Social Fund [ESF]) through the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020 in the context of the project "Investigation and Development of an Intelligent System for Fault Detection, Diagnosing and Prognosing in Industrial Induction Motors" (MIS 5050019).
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες