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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 3,202 Documents
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Fault detection and diagnosis in three phase IM using DSP: hardware implementation and real-time approach P. Thakre, Mohan; Kumar, Badal; Nilesh Thakur, Supriya; Kumar, Alok; Sharma, Padmini; Kedarnath Magadum, Prashant
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12131

Abstract

The study investigates real-time problem diagnosis of induction motors (IMs) with digital signal processing (DSP) to improve monitoring. IMs are essential to industrial applications but can fail owing to mechanical, electrical, and thermal stressors. These defects must be detected quickly to prevent motor failure and production downtime. DSP is used to create a sophisticated real-time online condition monitoring system to diagnose three-phase IM issues. The suggested system was validated using MATLAB calculations and experimental investigations on a 415 V, 1 HP, 50 Hz, 1440 rpm, 4-pole IM. Disruptions in the stator windings, such as inter-turn short circuits or inter-phase faults, as well as problems with the rotor, such as broken bars or end rings, are identified in this investigation. Keeping an eye on negative sequence currents and analyzing fault frequencies with a fast Fourier transform (FFT). According to the results of the testing, current approaches are not very good at detecting stator inter-turn difficulties under light-load and no-load conditions. Under varying loads, the proposed DSP-based system identified stator inter-turn, inter-phase, and broken rotor bar problems. Results showed that the DSP-based online condition monitoring system was more accurate and better at detecting faults than earlier methods, making it a good fit for usage in industrial applications.
Sailfish optimized MobileNet for robust recognition of American sign language alphabets and digits Sabura Banu Urundai Meeran; Selvaraj Kavitha; Balakrishnan Chinthamani; Balasubramanian Suresh Chander Kapali
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11043

Abstract

American sign language (ASL) is widely used for communication among the deaf and hard-of-hearing communities. This study aims to develop an optimized deep learning (DL) model for recognizing 36 static ASL gestures representing English alphabets (A–Z) and digits (0–9). A publicly available ASL dataset was used, and five convolutional neural network (CNN) architectures—AlexNet, GoogleNet, Inception V3, MobileNet, and ResNet50—were implemented as baselines. MobileNet was further optimized using the sailfish optimization (SFO) algorithm to fine-tune key hyperparameters and architectural settings. The models were evaluated using accuracy, macro precision, recall, F1-score, specificity, Cohen’s Kappa, Matthews correlation coefficient (MCC), balanced accuracy, Jaccard index, and error rate. The SFO-enhanced MobileNet achieved the highest performance, with 98.28% accuracy, 98.27% macro F1-score, 99.95% specificity, and a 1.72% error rate, outperforming all baselines across metrics. These results demonstrate that SFO optimization significantly improves MobileNet’s ability to classify ASL gestures accurately and efficiently. The proposed model’s high accuracy, robustness, and low inference time (22 ms) make it suitable for real-time sign language interpretation tools and assistive communication devices, supporting broader accessibility and inclusivity.

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