Koppert, StevenStevenKoppertHenke, ChristianChristianHenkeTrächtler, A.A.TrächtlerMöhringer, S.S.Möhringer2022-09-212022-09-212022https://publica.fraunhofer.de/handle/publica/42593210.1016/j.ifacol.2022.04.2522-s2.0-85132202718In this study, we describe the development of a Deep Convolutional Neural Network (DCNN)-based system that gives warnings about soon to be necessary blade changes of log bandsaw machines. The major difficulties in monitoring the blade wear arise from the complex domain of primary wood machining and a challenging small dataset. The developed concatenated DCNN can accurately indicate necessary saw blade changes, whereas purely feature-based and Multi-Layer-Perceptron-based methods fail. The system can be applied directly without complex preprocessing. To understand the effects leading to the DCNN's success, we also present an analysis of the prediction behavior in comparison to classical approaches.enArtificial Neural NetworksCondition MonitoringDeep Convolutional Neural NetworksNetwork ConcatenationPrimary wood machiningTool wear monitoringWood Bandsawsjournal article