NooJ Grammars for Italian Negation System and Sentiment Analysis

Monteleone, Mario; Mirto, Ignazio Mauro

doi:10.1007/978-3-030-92861-2_4

Cited by 1 publication

(1 citation statement)

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“…While retaining the general approach and some ontologies (subject to review), every other aspect underwent thorough redesign, including technology, algorithms, data structures, source code, and interface. The scientific underpinnings of AUTOMETA trace back to studies on Lexicon-Grammar (LG) ( [68][69][70][71][72]), mainly those dealing with the creation and use of terminological electronic dictionaries, and finite automata-based string matching ( [73][74][75][76][77][78][79]).…”

Section: Scientific Foundations Of the Researchmentioning

confidence: 99%

Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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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.

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