**Athanasios Karlis**

Athanasios D. Karlis (born in 1967), received his Dipl.-Eng. and PhD degrees from the Electrical & Computer Engineering Department of the Aristotle University of Thessaloniki, Greece, in 1991 and 1996, respectively. In 2000, he was made a Lecturer in the Department of Electrical & Computer Engineering at Democritus University of Thrace, Greece. In 2017, he was made an Associate Professor, and since 2019, he has been Director of the Electrical Machines Laboratory. His research interests are in diagnostics, the control of electrical machines and drives, renewable energies, and power production from small hydro-wind energy conversion and photovoltaics power systems. His research activity includes more than 60 papers in scientific journals, book chapters and international conference proceedings. As a visiting researcher, he traveled to the Helsinki University of Technology; the Centre for Rapid Transit Systems in the Department of Electrical & Computer Engineering of the Virginia Polytechnic Institute and State University (Virginia Tech); and the Friedrich-Alexander-Universitat Erlangen-N ¨ urnberg, Germany. He speaks Greek (native), ¨ English and German. He has supervised more than 60 final-year and 10 master's theses. He is currently supervising five PhD and two master's theses.

He has been a member of IEEE since 2000, and a senior member since 2012. He is a member of the Editorial Board of two international scientific journals. He was Chair of the Local Organizing Committee of the International Conference on Electrical Machines (ICEM 2018), which was held in Alexandroupoli, Greece, 2018. He served for 2 years as a member of the board of directors of the Hellenic Institute of Electric Vehicles (HEL.I.E.V.). In 2015, he received the IEEE Outstanding Branch Chapter Advisor Award for IEEE Region 8, and in 2016, the Outstanding Student Branch Chapter Advisor Award for IEEE Industry Applications Society.

### **Preface to "Advances in the Field of Electrical Machines and Drives"**

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications.

> **Athanasios Karlis** *Editor*

### *Article* **Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors**

**Angela Navarro-Navarro 1, Israel Zamudio-Ramirez 1,2, Vicente Biot-Monterde 1, Roque A. Osornio-Rios 2 and Jose A. Antonino-Daviu 1,\***


**Abstract:** Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case).

**Keywords:** current signals; stray flux signals; LDA; automatic fault diagnosis; induction motor; broken rotor bars; soft-starters
