Abstract
The main goal of each company is to generate profit and therefore it is necessary to optimize the value chain. In the manufacturing industry, the production processes have the biggest influence of the company’s success, thus every interruption in the production processes can lead to expensive consequences. Through industry 4.0 technologies it is possible to optimize the production processes by technically monitoring machines and their productions processes for example in the aspect of predictive maintenance or quality prediction. But each machine, each produced component, each prediction scenario has his own data fingerprint thus a universal machine learning scenario cannot be used. To solve this problem, an interactive web-based machine learning tool was developed, which allows to picture the corner of the fingerprints of each machine, each component in a central master data management and includes a project system where different machine learning scenarios can be defined, evaluated, and set productive. In addition, it contains an intelligent machine learning engine which automatically suggests possible machine learning algorithms for the monitoring scenario. It also includes a preprocessing engine, where static datasets can be analyzed, the optimal preprocessing parameters can be defined, and later used for the streaming data. A first evaluation of the implemented prototype shows, that it is possible to create individual machine learning projects with less work for different monitoring scenarios.
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Burdack, M., Rössle, M. (2019). A Concept of an Interactive Web-Based Machine Learning Tool for Individual Machine and Production Monitoring. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-13-8303-8_16
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DOI: https://doi.org/10.1007/978-981-13-8303-8_16
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