Daily Archives: May 11, 2026

Authors: Prof. G. R. Padule, Shweta Anil Bhosale, Dnyaneshwari Ravikant Patil, Vrushali Vishal Zambare

Abstract: Induction motors are vital components in industrial and commercial systems, where unexpected failures can lead to costly downtime and reduced productivity. Traditional maintenance strategies such as corrective and preventive maintenance are often inefficient, either reacting too late or performing unnecessary servicing. Predictive maintenance, powered by machine learning (ML) techniques, offers a smarter approach by forecasting motor health conditions based on real-time data analysis. This review paper presents an overview of recent advancements in predictive maintenance for induction motors using ML algorithms. Various techniques such as support vector machines (SVM), artificial neural networks (ANN), random forests, and deep learning models are discussed for fault detection, diagnosis, and remaining useful life (RUL) estimation. The paper also highlights the importance of feature extraction from vibration, current, and temperature signals, as well as the integration of Internet of Things (IoT) and cloud computing for real-time monitoring. Comparative analysis of different ML approaches is provided to identify their strengths, limitations, and potential for industrial application. Finally, the review outlines current challenges and future research directions for developing efficient, scalable, and interpretable predictive maintenance frameworks for induction motors.

Published by: vikaspatanker

Authors: Mr. Prajwal Narayan Chaudhary, Mr. Pranav Prasad Kulkarni, Mr. Chetan Ashok Bhalekar, Mr. Aditya Ganesh Gunjal, Professor Kalyani Zirpe

Abstract: Corrosion is a significant cause of damage in industrial infrastructure, transportation systems, marine equipment, pipelines, and metal parts. Traditional methods for inspecting corrosion mainly rely on manual observation and regular maintenance. These processes are time-consuming, labor intensive, and are subjective, which can lead to human error. Delays in spotting corrosion can lead to serious structural failures, higher maintenance costs, operational downtime, and safety risks. To address these issues, this paper introduces a real-time AI-based Corrosion Detection and Monitoring System. This system uses the YOLOv5 deep learning framework along with a modern web-based structure. The new system combines computer vision, deep learning, and web technologies to automate the detection of corrosion and assess its severity. It uses the YOLOv5s object detection model to find corrosion areas in uploaded images and live camera feeds. A React.js frontend offers an engaging and responsive user interface. Meanwhile, a FastAPI backend handles image processing, runs the necessary calculations, and communicates results. The system evaluates detected corrosion areas using bounding box calculations to estimate the amount of corrosion and categorize its severity as mild, moderate, or severe. It also features graphical visualizations, historical tracking, and repair suggestions to support preventive maintenance. This framework provides nearly real-time detection with higher accuracy and less reliance on manual inspection. Its modular and scalable design allows it to be used in various industries, including maritime, civil infrastructure, manufacturing, automotive, and aviation. Tests show that the system successfully identifies corrosion under different environmental conditions while maintaining good computational performance. This solution represents a cost- effective and smart way to monitor structural health and perform predictive maintenance.

DOI: https://zenodo.org/records/20121779

Published by: vikaspatanker

Authors: Tanu Yadav, Neelam Sahu, Deepak Sahu

Abstract: The rapid expansion of the pre-owned automobile industry has increased the demand for reliable and intelligent digital platforms for vehicle trading. Traditional used-car marketplaces often face challenges such as lack of transparency, inefficient search mechanisms, inconsistent pricing, and fraudulent listings, which reduce user trust and overall customer satisfaction. This research proposes an AI- powered car marketplace designed to improve the process of buying, selling, and exchanging second- hand vehicles through intelligent automation and secure digital infrastructure. The proposed system integrates advanced technologies including intelligent search optimization, personalized recommendation systems, automated listing moderation, and secure authentication mechanisms to enhance platform reliability and usability. The platform provides users with detailed vehicle listings, filtering and comparison features, responsive communication channels, and mobile-friendly accessibility to simplify customer interaction and decision-making. The backend architecture is developed to support scalable data management and efficient transaction handling using modern web technologies. Artificial Intelligence modules are incorporated to improve recommendation accuracy, optimize search relevance, and identify suspicious or duplicate listings. Experimental evaluation indicates that the proposed system improves search efficiency, recommendation precision, and operational transparency compared to conventional online used-car trading systems. The research demonstrates how AI-driven digital marketplaces can enhance trust, user engagement, and efficiency within the pre-owned vehicle industry while providing a scalable solution suitable for modern automotive e-commerce applications.

Published by: vikaspatanker

Authors: M.R.Manas, Dr. Umakanta Choudhury

Abstract: This study provides an in-depth analysis of the electromagnetic comparative assessment of the unskewed and skewed rotors for a 1 kW, three-phase, inner-rotor permanent magnet synchronous generator intended for small-scale direct-drive wind power applications. The generator has 36 stator slots and 12 rotor poles, with a 220 mm outer diameter of the stator and a stack length of 60 mm. The unskewed generator uses a ring magnet rotor design and features a gap size of 2.0 mm, while the skewed rotor design uses a block magnet rotor with a linear step of 10 degrees in three stages, with the air gap size of 1.5 mm. Performance criteria used for the finite-element-based simulations using Altair FluxMotor include the following: cogging torque, back-EMF waveform quality, losses, torque ripple, voltage, and efficiency, combined with thermal analysis. The reduction of the peak-to-peak cogging torque of the skewed rotor reaches 84.5phase back-EMF decreases by 67requirements of IEEE 519 regarding harmonics. Both the unskewed and skewed rotors show comparable efficiency at the same operating point (19 N·m and 500 rpm): 95.34full-load efficiency of the unskewed rotor (92.87the corresponding efficiency of the skewed rotor (91.96.

DOI: https://doi.org/10.5281/zenodo.20120373

Published by: vikaspatanker

Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: Clinical time-series data are inherently complex, characterized by temporal dependences, irregular sampling and missing observations making accurate longitudinal risk prediction a challenging task. The study presents a novel hybrid Transformer framework for temporal representation learning and longitudinal risk prediction in clinical time-series that integrates the strengths of self-attention mechanism of Transformers to capture long-range interactions across time steps with the LSTM networks in modeling short-term temporal dependencies. A fusion module is introduced to adaptively combine representations from both components, enabling robust learning from irregular and partially observed clinical data. The experimental results demonstrate that the hybrid transformer framework effectively categorized patients into high-risk and low-risk categories based on their attributes. The training results indicate that the model performed well, with an accuracy of 98.6%, a sensitivity of 96.2% and a specificity of 97.8%. The model correctly identified 11 out of 18 high-risk patients and 16 out of 22 low-risk patients, with apparent errors of 38.9% and 27.3% respectively. These findings indicate that the hybrid Transformer framework can successfully learn patterns associated with cardiovascular risk from training data. Similarly, the test results confirm the model’s ability to predict previously unseen data. The model correctly categorized 9 out of 12 high-risk cases and 6 out of 8 low-risk cases, resulting an overall accuracy of 91.2%, sensitivity of 89.3% and specificity of 92.0% with a 25% apparent error in both cases.

DOI: https://doi.org/10.5281/zenodo.20118703

Published by: vikaspatanker

Authors: Priyanka N Godiyal, Rutuja Amrale, Revati Ma’am, Archana Ma’am.

Abstract: Road traffic accidents are a leading cause of mortality worldwide, with India recording over 1.5 lakh fatalities annually. Identifying 'black spots' — specific road segments with disproportionately high accident frequency — is critical for targeted infrastructure intervention. Traditional methods of black spot identification rely on statistical thresholds applied to historical data, which are often reactive and location-agnostic. This paper proposes an integrated framework combining Machine Learning (ML) and Geographic Information Systems (GIS) for predictive black spot detection. We review and compare ML algorithms including Random Forest, XGBoost, Support Vector Machines (SVM), and Deep Neural Networks applied to multi-source data comprising accident records, road geometry, traffic volume, and environmental factors. Spatial analysis techniques such as Kernel Density Estimation (KDE) and spatial autocorrelation are used for feature engineering. Results show that ensemble methods achieve accuracy above 90%, with XGBoost yielding the highest AUC-ROC of 0.94. GIS-integrated output maps provide actionable, zone- specific risk rankings to support road safety planning.

DOI: https://doi.org/10.5281/zenodo.20118287

Published by: vikaspatanker