Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the Artificial Neural Network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. Meanwhile, the Decision Tree (DT) model showed the lowest accuracy at 0.89. Further model evaluation was carried out using a confusion matrix, which provided a detailed breakdown of the models' performance for each class, revealing the number of correctly and incorrectly classified induction motor conditions. The results from the confusion matrix indicate that the models effectively classified the various states and conditions of the induction motors. To enhance model performance in future work, potential avenues include refining the ANN and RF models, exploring transfer learning or ensemble methods, and incorporating diverse datasets to improve generalization.

This paper presents an adequate mathematical representation of a quadcopter’s system dynamics and effective control techniques. A quadcopter is an unmanned aerial vehicle (UAV) that is able to do vertical take-off and landing. This study presents a nonlinear quadcopter system’s mathematical modeling and control for stabilization and trajectory tracking. The mathematical model of the system dynamics of the quadcopter is derived using Newton and Euler equations with proper references to the appropriate frame or coordinate system. A PD control algorithm is developed for the nonlinear system for stabilization. Another nonlinear control technique called full state feedback linearization (FBL) using nonlinear dynamic inversion (NDI) is developed and implemented on the quadcopter system. However, there is a problem with the normal approach of the complete derivation of the full state FBL system using NDI as gathered from the literature review. In such an approach, the PD controller that was used for attitude stabilization was able to stabilize the angles to zero states, but the position variables cannot be stabilized because the state variables are not observable. Thus, a new approach where the position variables are mapped to the angle variables which are controllable so as to drive all states to zero stability was proposed in this study. The aim of the study was achieved but the downside is that it takes a longer time to achieve this stability so it is not efficient and should only be considered when absolute zero stability is the aim without considering time efficiency. The study further investigates the problem of nonlinear quadcopter system’s mathematical modelling and control for stabilization and trajectory tracking using the feedback linearization (FBL) technique combined with the PD controller. The proposed control algorithms are implemented on the quadcopter model using MATLAB and analyzed in terms of system stabilization and trajectory tracking. The PD controller produces satisfactory results for system stabilization, but the FBL system combined with the PD controller performs better for trajectory tracking of the quadcopter system.

The present Nigerian transmission network is faced with the difficulty of evacuating and dispatching reliable and quality electricity supply and simultaneously maintaining an operational standard of security to prevent any collapses. Therefore, this study developed a novel technique to optimize electrical current flow to provide in-depth research and analysis of current flowing in the transmission network circuit prone to danger during short-circuit faults. The research methodology involved the generation of unbalanced short-circuit calculations at every single node of the three-phase network using the symmetrical component method. Numerical simulation of different types of unbalanced short-circuit fault into the entire 330kV transmission network using unbalanced fault algorithms written in a flexible MATLAB program environment is also performed on every bus. The influence of these short-circuit faults is examined on the generated spectrum of line current magnitude. This study then generates a series of unbalanced current circuit and line losses analysis that unveils the different scenarios regarding existing network performance. The method adopted is promising. It established the most critical lines (about 20) with high unbalanced current magnitudes and high line losses during the disturbance. Based on the result analysis, four (quad) bundles of conductors is designed as a proposed modification to the upgrade of all critical double circuit lines and the conversion of single critical lines on the 330kV transmission network to improve the power transfer capability and also meet the future transmission network development plan. Furthermore, recommendations that are considered desirable in this study are proffered to ensure acceptable power quality and security in the network.

Managing people and keeping adequate record of the attendance for most of the tertiary institutions are difficult task. Moreover, the manual taking of attendance adds to the difficulty of this task because it is strenuous, time intensive and can easily be falsified. Meanwhile, With the advent of new technology, a new world of authentication and security has been created via biometrics. This work aims at improving Covenant Universities paper-based attendance by using biometrics, specifically fingerprint technology. It is a web application that works with a fingerprint reader from which the features of the student’s fingerprint are extracted during enrolment. This is also needed during the authentication phase which occurs during the roll call attendance in the halls of residence. It is implemented using PHP and MySQL.

The power sector in many developing nations faces challenges in meeting consumers’ demands for a reliable electricity supply due to increased load demand, primarily driven by population growth. Urban areas receive 24 h power, while extensive losses within the system limit coverage. Setting up centralised stations offers a temporary solution, but the capacity of the ageing transmission lines is uncertain. This study proposes integrating distributed generators (DG) into the power system, using Inherent Structural Network Topology (ISNT) for DG siting and a modified Forward-Backward Sweep model for sizing. Voltage Stability Index (VPI) assesses network stability. The model considers voltage profile and line losses, optimizing DG sites and sizes. Results demonstrate the model’s efficacy, offering insights for optimal DG planning to minimize losses and enhance voltage profiles. The study informs power system engineers for future planning, aiding decisions on DG location and size, potentially reducing line losses and improving voltage profiles, thus assisting in network upgrades or expansions.