Predictive Maintenance with Linguistic Text Mining

Alberto Postiglione () and Mario Monteleone
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Alberto Postiglione: Department of Business Science and Management & Innovation Systems, University of Salerno, Via San Giovanni Paolo II, 132, 84084 Fisciano, Italy
Mario Monteleone: Department of Political and Communication Sciences, University of Salerno, Via San Giovanni Paolo II, 132, 84084 Fisciano, Italy

Mathematics, 2024, vol. 12, issue 7, 1-17

Abstract: The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

Keywords: predictive maintenance; text mining; finite automata; natural language processing; log message; cyber-physical systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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