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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Bearing diagnostics using machine learning
 Zonygová, Kristýna ; Marada, Tomáš (referee) ; Zuth, Daniel (advisor)
The Master's thesis deals with the use of artificial intelligence methods in order to classify bearing failures. The SVC (Support Vector Classification), KNN (K-Nearest Neighbors Classifier), RFC (Random Forest Classifier) and CNN (Convolutional Neural Network) classification methods are described and tested on ball-bearing vibration signals from two different datasets. All methods achieve quite well accuracy (from 94.1 % to 99.8 %). Scripts in the Python programming environment that use libraries with free-licenses are also included. They provide the possibility of training classification methods (SVC, KNN, RFC or CNN) on your own data, or the use of already trained models.
Detailed record
Bearing diagnostics using machine learning
 Zonygová, Kristýna ; Marada, Tomáš (referee) ; Zuth, Daniel (advisor)
The Master's thesis deals with the use of artificial intelligence methods in order to classify bearing failures. The SVC (Support Vector Classification), KNN (K-Nearest Neighbors Classifier), RFC (Random Forest Classifier) and CNN (Convolutional Neural Network) classification methods are described and tested on ball-bearing vibration signals from two different datasets. All methods achieve quite well accuracy (from 94.1 % to 99.8 %). Scripts in the Python programming environment that use libraries with free-licenses are also included. They provide the possibility of training classification methods (SVC, KNN, RFC or CNN) on your own data, or the use of already trained models.
Detailed record

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