Prediction of the voltage status of a three-phase induction motor using data mining algorithms
Data mining has found application in many research fields for predictive analysis. In engineering, data mining has been applied for equipment fault prediction by using the historical fault data of the equipment to train a data mining model for predicting future events. Power supply variations, and p...
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| Veröffentlicht in: | SN applied sciences 2019-12, Vol.1 (12), p.1675, Article 1675 |
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| creator | Adekitan, Aderibigbe Israel Abdulkareem, Ademola |
| description | Data mining has found application in many research fields for predictive analysis. In engineering, data mining has been applied for equipment fault prediction by using the historical fault data of the equipment to train a data mining model for predicting future events. Power supply variations, and power quality issues affect the performance of a three-phase induction motor (TPIM). In this study, the operational performance data of a TPIM was deployed as a dataset for training a Konstanz Information Miner based model, for predicting the voltage status of the motor. The prevailing voltage status is classified into three, and these are: under voltage (2–10%), rated voltage, or over voltage (2–10%). For comparative analysis, the Tree Ensemble, Decision Tree, Random Forest and Support Vector Machine (SVM) were deployed for the voltage prediction. The result shows that the SVM had the highest prediction accuracy of 84.85%. This creates a platform for developing embedded systems that are trained using knowledge acquired from data mining for performance monitoring of induction motors. |
| doi_str_mv | 10.1007/s42452-019-1720-9 |
| format | Article |
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In engineering, data mining has been applied for equipment fault prediction by using the historical fault data of the equipment to train a data mining model for predicting future events. Power supply variations, and power quality issues affect the performance of a three-phase induction motor (TPIM). In this study, the operational performance data of a TPIM was deployed as a dataset for training a Konstanz Information Miner based model, for predicting the voltage status of the motor. The prevailing voltage status is classified into three, and these are: under voltage (2–10%), rated voltage, or over voltage (2–10%). For comparative analysis, the Tree Ensemble, Decision Tree, Random Forest and Support Vector Machine (SVM) were deployed for the voltage prediction. The result shows that the SVM had the highest prediction accuracy of 84.85%. 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| subjects | Algorithms Applied and Technical Physics Big Data and Applied Deep Learning: From Science to Applications Chemistry/Food Science Comparative analysis Data acquisition Data analysis Data mining Datasets Decision analysis Decision trees Earth Sciences Economic growth Electric potential Energy consumption Engineering Engineering: Data Science Environment Fault diagnosis Hypothesis testing Induction motors Knowledge acquisition Knowledge discovery Machine learning Manufacturing Materials Science Power supply Predictions Research Article Support vector machines Voltage |
| title | Prediction of the voltage status of a three-phase induction motor using data mining algorithms |
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