Machine learning algorithms benchmarking for real-time fault predictable scheduling on a shop floor

To select a proper machine learning algorithm for fault predictable scheduling on a shop floor, ten algorithms in the machine learning field have been selected, implemented and compared in this research. Due to the lack of applicable real data to the authors, a data generation method is proposed in terms of data complexity, number of data attributes and data depth. On top of the method, six datasets are generated by selecting three-level data attributes and three-level data depths, which were used to train the ten algorithms. The performances of the algorithms are evaluated by considering three indexes including, training accuracy, testing time and training time. The results demonstrate that naive Bayes classifier is suitable to low-complexity data and that convolutional neural network and deep belief network fit well in high-complexity data, such as the real data.