Automatic recommendation of prognosis measures for mechanical components based on massive text mining

作     者：Martinez-Gil, Jorge Freudenthaler, Bernhard Natschlaeger, Thomas 

作者机构：Software Competence Ctr Hagenberg GmbH Hagenberg Austria 

出 版 物：《INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS》 (国际网络信息系统杂志)

年 卷 期：2018年第14卷第4期

页      面：480-494页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Austrian Research Promotion Agency Austrian Ministry for Transport, Innovation and Technology Federal Ministry of Science, Research and Economy Province of Upper Austria 

主　　题：Web mining Web search and information extraction Web data integration 

摘      要：Purpose The purpose of this study is to automatically provide suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Design/methodology/approach Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. Findings This work proposes a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. Originality/value The great advantage of text mining is that makes possible to automatically analyze vast amounts of unstructured information to find corrective strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.