Dr. Ioannis Eirinaios Chasiotis
Electrical Machines Laboratory, Department of Electrical and Computer Engineering, Democritus University of Thrace, 671 00 Xanthi, Greece
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Academic Career Length
11Years (2016-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2021 in Procedia Computer Science
Journal Article
MDMaria Drakaki
Maria DrakakiYKYannis L. Karnavas
Yannis L. Karnavas
PTPanagiotis Tzionas
Panagiotis Tzionas
Ioannis Eirinaios Chasiotis16 January 2020 in IET Electric Power Applications
Journal Article
MDMaria Drakaki
Maria Drakaki
Yannis Karnavas
Athanasios KarlisIoannis Eirinaios ChasiotisPTPanagiotis Tzionas
Panagiotis Tzionas
A critical factor in industrial production maintenance is decision‐making in fault diagnosis. Motor current signature analysis (MCSA) is an established condition‐based maintenance method to make fault diagnosis in induction motors (IMs). The occurrence of multiple interacting faults, as well as emergent behaviour, can be tackled efficiently especially when MCSA is combined with distributed problem‐solving artificial intelligence (AI) techniques. Therefore, in this study, an intelligent multi‐agent system (MAS) is presented to make decisions on the fault conditioning of a three‐phase squirrel cage IM. The incorporated agents represent different health conditions of the same IM, with faults that may occur in the rotor bars. All agents utilise an AI method, and use for training MCSA experimental data. A supervisor agent (SA) initially communicates with agents employing feed‐forward artificial neural networks trained with the back‐propagation algorithm and performs the final fault diagnosis by evaluating their responses. When a decision cannot be made on the fault type, the SA employs another agent that uses the k‐nearest neighbour rule. The proposed method achieves high fault diagnosis accuracy. Its performance is also compared with results previously obtained from the same motor when an adaptive neuro‐fuzzy inference system combined with a subtractive clustering method had been used.
24 January 2019 in IEEE Transactions on Transportation Electrification
Journal Article
Ioannis Eirinaios ChasiotisYannis KarnavasPod propulsion systems are widely used, since they exhibit high efficiency and reliability especially when they are combined with permanent magnet synchronous motors (PMSMs). Except from high efficiency, high power density and increased fault-tolerance capability are also the main requirements for a motor in order to be used in such an application. Generally, PMSMs present high power density in high speed operation. However, in low-speed applications the achievement of increased power density is not an easy task. Moreover, PMSMs with surface-mounted magnets are characterized by low winding selfinductance which limits the fault-tolerance capability. Thus, this paper presents an effective generic multi-criteria PMSM design approach and proposes a global selection strategy for both the slots/poles combination and the motor’s airgap diameter to axial length ratio aiming to enhance the two aforementioned characteristics. At the same time, numerous practical and physical constraints have been thoroughly described and taken into account in the proposed process. These constraints are not adequately presented in the relative literature. The derived topologies are validated through FEM analysis and several important quantities are calculated and used as comparison metrics. Finally, useful conclusions are extracted that could be of great help for designers and manufacturers of pod propulsion systems.
22 March 2021 in IET Electric Power Applications
Journal Article
Yannis KarnavasSPSpyridon Plakias
Spyridon Plakias
Ioannis Eirinaios ChasiotisA 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.
30 October 2019 in Journal of Electromagnetic Waves and Applications
Journal Article
MPMaria C. Pechlivanidou
Maria C. Pechlivanidou
Ioannis Eirinaios ChasiotisYannis KarnavasPermanent magnet synchronous motors (PMSMs) have recently gained increasing industrial and research interest. The PMSM design and especially its performance evaluation through two-dimensional (2D) and three-dimensional (3D) magnetic field analysis using finite element method (FEM) is not trivial, as it is a quite demanding and time consuming procedure. Furthermore, there has always been doubt on the accuracy of 2D analysis results and on which type of analysis is the most preferable one for the specific motor. This work aims to emphasize the difficulties and the advantages that arise from the 3D analysis. The aforementioned simulations were performed for the case of a four-pole PMSM. Several magnetic and electromechanical quantities have been determined. Additionally, various materials were utilized for the construction of stator and rotor cores and their effect on the motor’s behavior was investigated. Finally, the derived results are compared and useful conclusions regarding the solution accuracy are given.
3 November 2016
Conference Paper
Yannis KarnavasIoannis Eirinaios ChasiotisEPEmmanouil L. Peponakis
Emmanouil L. Peponakis
This paper proposes a simple though effective cooling system, which has been designed for a light permanent magnet synchronous motor (PMSM) with outer rotor topology, for an in-wheel electric vehicle (EV) application. Various thermal considerations, found in the recent literature and a large number of variables are taken into account in order to ensure that the cooling system meets the special features for this kind of motor. In this context, numerous simulations using Finite Elements Method (FEM) are conducted first, for the determination of temperature distribution at the various parts of the machine under different operating conditions. Next, an investigation of the proposed cooling system performance is done by examining some parameters, such as the coolant's inlet temperature and the corresponding flow rate. Based on these results the final specifications of the cooling system are determined. Finally, the cooling system's efficiency according to the motor's output power range is examined.
About Me
Ioannis D. Chasiotis received the Diploma Degree and Integrated Master of Electrical and Computer Engineering from the Department of Electrical and Computer Engineering (DECE) of Democritus University of Thrace (DUTh), Xanthi, Greece in 2015. He received the Ph.D. degree from the same department in 2021. During his post-graduate studies, he has gained: a) 1-year award scholarship from Bodossaki Foundation (period: 01/09/2015 - 31/08/2016), b) full scholarship from Onassis Foundation (period: 01/10/2017 - 29/02/2020) and c) scholarship in the context of “1st Announcement of Hellenic Foundation for Research and Innovation for Ph.D. Candidates” (period: 03/08/2017 - 31/03/2018). Since December 2021, he serves as Postdoctoral Researcher in Electrical Machines Laboratory of DECE/DUTh. His research interests include: i) the modeling and design of electrical machines, ii) the analysis and design of integrated direct-drive systems for traction and propulsion applications, iii) the high power density and high efficiency electric motors design, iv) the parameters estimation and fault diagnosis of electrical machines, v) the development of drive systems for electrified transportation and vi) the optimization algorithms and artificial intelligence methods application on the above topics. He has published scientific works in numerous international scientific journals, in proceedings of international scientific conferences as well as book chapters in international engineering books.