C4.5: programs for machine learning
C4.5: programs for machine learning
Intelligent data analysis with fuzzy decision trees
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Artificial neural network approach for fault detection in rotary system
Applied Soft Computing
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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Monoblock centrifugal pumps play a very critical role in a variety of applications and condition monitoring of the various mechanical components of centrifugal pump becomes essential which in turn increases the productivity and reduces the breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks fuzzy logic was employed for continuous monitoring and fault diagnosis. This paper presents the use of support vector machine (SVM) algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies were computed for different types of classifiers such as artificial neural network (ANN), support vector machine (SVM) and J48 decision tree algorithm.