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    <title language="eng">A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications</title>
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    <author>Lukas Meitz</author>
    <author>Michael Heider</author>
    <author>Thorsten Schöler</author>
    <author>Jörg Hähner</author>
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      <value>product automation systems</value>
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    <subject>
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    <collection role="persons" number="schöler">Schöler, Thorsten (Prof. Dr.-Ing. Hon. Dr. of ONPU)</collection>
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    <title language="eng">On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines</title>
    <abstract language="eng">Maintenance of complex machinery is time and resource intensive. Therefore, decreasing maintenance cycles by employing Predictive Maintenance (PdM) is sought after by many manufacturers of machines and can be a valuable selling point. However, currently PdM is a hard to solve problem getting increasingly harder with the complexity of the maintained system. One challenge is to adequately prepare data for model training and analysis. In this paper, we propose the use of expert knowledge–based preprocessing techniques to extend the standard data science–workflow. We define complex multi-purpose machinery as an pplication domain and test our proposed techniques on real-world data generated by numerous achines deployed in the wild. We find that our techniques enable and enhance model training.</abstract>
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    <author>Lukas Meitz</author>
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    <title language="eng">A Literature Review Framework and Open Research Challenges for Predictive Maintenance in Industry 4.0</title>
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