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

Abstract

<span lang="EN-GB">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 learning</span>

The impact of engineering students' performance in the first three years on their graduation result using educational data mining

“…The discovery of hidden information is achieved by running data mining algorithms that combine statistics with computer science to mine valuable information from a seemingly meaningless data jumble. Data mining can be applied in various fields such as engineering (Adekitan et al., 2019; Saini and Aggarwal, 2018), business management (Zuo et al., 2016), marketing and product design (Jin et al., 2019), computer science (Mahendra et al., 2019), education (Ibrahim et al., 2019; Porouhan, 2018), genetics (Noreña et al., 2018), biological studies (Gu et al., 2018), facility maintenance management (Miguel-Cruz et al., 2019), health and drug development studies (Keserci et al., 2017), chemistry and toxicity analysis (Saini and Srivastava, 2019), meteorology (Kovalchuk et al., 2019), transportation safety (Divya et al., 2019) and traffic management (Amiruzzaman, 2019), fraud detection (Vardhani et al., 2019), and so forth. In the educational sector, volumes of data are daily generated from various teaching and learning activities within an institution.…”

Performance evaluation of different classification techniques using different datasets

“…The knowledge acquired from the training was applied in predicting the type of supply that produced the remaining 30% of the motor operational data samples. The predictive accuracy achieved in the paper is indicative of the suitability of data mining approach for motor performance monitoring [18].…”

Convolutional recurrent neural network with template based representation for complex question answering

“…The convolution window size of each layer is 5x100, 4x1, 2x1 and the pooling window size is 4x100, 3x1, 3x1. This configuration is also denoted as (5,4,2) and (4,3,3). The RNN is modelled with 2 LSTM units and the size of all LSTM units is set to 360.…”

“…When compared with the traditional information retrieval system, the relevant amount of information is considered as an answer instead of retrieving the entire document [2]. Data mining is a subfield of computer science that enables intelligent extraction of useful information [3]. The user is expecting correct, comprehensible and concise answer which may indicates the sentence, image, word, paragraph, audio fragment or the whole document [4].…”