Citation: Asad, B.; Raja, H.A.; Vaimann, T.; Kallaste, A.; Pomarnacki, R.; Hyunh, V.K. A Current Spectrum-Based Algorithm for Fault Detection of Electrical Machines Using Low-Power Data Acquisition Devices. Electronics 2023, 12, 1746. https://doi.org/10.3390/ electronics12071746 Academic Editors: Ryad Zemouri, Mélanie Lévesque and Arezki Merkhouf Received: 10 March 2023 Revised: 4 April 2023 Accepted: 4 April 2023 Published: 6 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article A Current Spectrum-Based Algorithm for Fault Detection of Electrical Machines Using Low-Power Data Acquisition Devices Bilal Asad 1,2, * , Hadi Ashraf Raja 2 , Toomas Vaimann 2 , Ants Kallaste 2 , Raimondas Pomarnacki 3 and Van Khang Hyunh 4 1 Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 2 Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia; hadi.raja@taltech.ee (H.A.R.); toomas.vaimann@taltech.ee (T.V.); ants.kallaste@taltech.ee (A.K.) 3 Department of Electronic Systems, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania; raimondas.pomarnacki@vilniustech.lt 4 Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway; huynh.khang@uia.no * Correspondence: bilal.asad@taltech.ee Abstract: An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented. Keywords: electrical machine; machine learning; data acquisition; FEM; signal processing; Arduino; artificial intelligence 1. Introduction The research in the predictive maintenance of electrical machines is touching new horizons. Cloud computation and distributed low-cost sensors are integral for Industry 4.0 standards. They can also be considered a paradigm shift in the predictive maintenance of electrical machines. Low-cost data acquisition sensors are becoming essential as elec- trical machines are becoming increasingly popular in small and medium-range electric vehicles. The research in the field of condition monitoring of electrical machines using stator currents [1–3], stator voltages [4–6], speed and torque ripples [7,8], stray flux [9–14], vibration analysis [15–19], thermal analysis [20–23], acoustic analysis [24–27], work in the steady-state interval [28], or transient regime [9,29–32] can be considered as mature enough after over a century of research. The research path started with conventional signal processing and harmonic estimation-based techniques. Here, the fundamental rule was to discover the fault-based new frequency components in the machine’s global signal. The signal processing techniques were explored by researchers extensively to secure or protect the tiny, sensitive, fragile, and load-dependent fault-based information. For this purpose, the improvement in the spectrum resolution both in stationary and transient Electronics 2023, 12, 1746. https://doi.org/10.3390/electronics12071746 https://www.mdpi.com/journal/electronics