Data mining and knowledge discovery in databases
Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Knowledge discovery techniques for predicting country investment risk
Computers and Industrial Engineering
Data Mining with SQL Server 2005
Data Mining with SQL Server 2005
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Exploring the risk factors of preterm birth using data mining
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper describes an approach for detection of the supply unbalance condition in induction motors by using data mining process. Simulation results have shown that a good indicator of the fault is the amplitude of the second harmonic of the supply frequency component (2f) in the signal obtained by the differences in supply current zero crossing instants. In the study, linear regression (LR), pace regression (PR), sequential minimal optimization (SMO), M5 model tree, M5'Rules, KStar, additive regression and back propagation neural network (BPNN) models are applied within the data mining process for determining the condition of the motor supply voltage. All data mining algorithms were applied using WEKA software. The best result for the determination of the fault related dominant parameter was obtained by using the M5P algorithm model.