Abstract
The aim of the chapter is to explain the basic concepts of Machine Learning applied to condition monitoring in Industry 4.0. Machine learning is a common term used today in different fields, mainly related to an automated and self-learning routine in a decisional process. This chapter details how a Machine Learning approach may be structured, starting from a distinction between Supervised and Unsupervised approaches. These two classes have different advantages and disadvantages that constrain their application to specific boundary conditions. Machine Learning techniques are the core part of a structured methodology for the condition monitoring, but other phases, such as the pre-processing of data, the feature extraction and the evaluation of performances, are equally important for the success of a condition monitoring system. Together with standard parameters used to assess the performances of the machine learning method, a particular emphasis will be given to the interpretability of the results that can be determinant in the choice and development of a specific tool for condition monitoring in an industrial environment.
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Capelli, L. et al. (2022). A Structured Approach to Machine Learning Condition Monitoring. In: Chaari, F., Chiementin, X., Zimroz, R., Bolaers, F., Haddar, M. (eds) Smart Monitoring of Rotating Machinery for Industry 4.0. Applied Condition Monitoring, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-79519-1_3
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DOI: https://doi.org/10.1007/978-3-030-79519-1_3
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