Determining the operational status of a three phase induction motor using a predictive data mining model

The operational performance of a three-phase induction motor is impaired by unbalanced voltage supply due to the generation of negative sequence currents, and negative sequence torque which increase motor losses and also trigger torque pulsations. In this study, data mining approach was applied in developing a predictive model using the historical, simulated operational data of a motor for classifying sample motor data under the appropriate type of voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to 5%). A dataset containing the values of a three-phase induction motor’s performance parameter values was analysed using KNIME (Konstanz Information Miner) analytics platform. Three predictive models; the Naïve Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors were deployed for comparative analysis. The dataset was divided into two; 70% for model training and learning, and 30% for performance evaluation. The three predictors had accuracies of 98.649%, 100% and 98.649% respectively, and this confirms the suitability of data mining methods for predictive evaluation of a three-phase induction motor’s performance using machine learnin

Face Recognition Using VGG16 CNN Architecture for Enhanced Security Surveillance—A Survey

A review of the web camera surveillance, face recognition, convolution neural network (CNN), digital images are presented in this work. Previous works on face recognition systems for enhanced surveillance-based security are presented together with relevant deep learning concepts and theories relating to convolutional neural networks. In-depth analysis is summarized and presented in concise way