Abstract
With the application of a new generation of information technology in the field of manufacturing and the deep integration of computer technology and manufacturing, industrial production is moving towards intellectualization and networking . Because the current production system cannot fully exploit the value of industrial data and the existence of information islands in the production process, this paper presents a study on the development, testing, and evaluation of a machine learning process that can be run on low-cost standard microcontrollers with limited computing and memory resources. This paper first analyzes the basic idea of whether it is possible to develop software for intelligent sensors whose algorithms run on microcontrollers. At the same time, it is considered whether the training and the adaptation of the model parameters can be done on the microcontroller to enable an online adaptation of the machine to be monitored. The goal is a closed system that does not need a backend and the storage of large amounts of data is not necessary.
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This research is partly supported by the Bulgarian National Science Fund in the scope of the project “Exploration the application of statistics and machine learning in electronics” under contract number КП-06-H42/1.
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Grethler, M., Marinov, M.B., Klumpp, V. (2021). Embedded Machine Learning for Machine Condition Monitoring. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-78459-1_16
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