Vanneschi, LeonardoHilzbrich, ThomasObenauff, Alexander2019-07-262020-06-272019-06-11http://hdl.handle.net/10362/76579Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsManufacturing companies face significant market pressure in today’s globalised world. Fierce global competition and product individualisation mean that production systems require continuous optimisation. This means that automation, flexibility and efficiency have all become vital elements for manufacturers. In this paper, a method based on incremental classification used for manufacturing errors is presented. The analysis and classification focus on data of binary form collected from a machine control unit during manufacturing operation in real time. Various methods that can learn from data incrementally and autonomously are to be applied. The training starts with the least amount of data possible and other important steps like data preprocessing are reviewed under the aspect of incremental learning.engProgressive Learning Incremental LearningFault DiagnosisManufacturingMachine LearningNovelty DetectionConcept DriftOnline ResamplingPLTNeural NetworkHoeffding TreeAdaptive Random Forestk-Nearest Neighboursmaster thesis202267768