文章基本信息

标题：NLP-based insights discovery for industrial asset and service improvement: an analysis of maintenance reports
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作者：Sala Roberto ; Pirola Fabiana ; Pezzotta Giuditta 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：2
页码：522-527
DOI：10.1016/j.ifacol.2022.04.247
语种：English
出版社：Elsevier
摘要：AbstractEven if usually filled in the form of unstructured text, maintenance service reports can be an important source of information for manufacturing companies providing field and remote maintenance services to their customers. By analyzing their content, companies can discover hidden knowledge that can be used for improvement purposes. By exploiting Natural Language Processing (NLP) techniques, this paper wants to show how an Italian company producing machinery can extract new knowledge from its maintenance database. The company under analysis started a servitization journey and need to understand how to improve the maintenance service delivery. By using the information extracted, the company can define improvement plans linked to both the maintenance service delivery and the asset design.
关键词：KeywordsProduct-Service SystemServitizationMaintenanceText MiningIndustry 4.0Natural Language ProcessingKnowledge ExtractionArtificial IntelligenceTopic ModellingLatent Dirichlet Allocation